Methods for detecting genomic dna methylation

ABSTRACT

The presently disclosed subject matter provides high-throughput methods for performing genomic DNA methylation assessments. The presently disclosed subject matter further provides methods for diagnosing a subject with a disease and/or disorder, and for determining the prognosis of a subject that has a disease and/or disorder. In certain embodiments, the present disclosure provides a diagnostic method that includes obtaining a biological sample from the subject; determining the methylation status of one or more genomic DNA loci in one or more cells of the biological sample; and diagnosing a disease and/or disorder in the subject, wherein the methylation status of the one or more genomic DNA loci indicates the presence of the disease and/or disorder in the subject.

PRIORITY CLAIM

This application claims priority under 35 U.S.C. §119 to U.S.Provisional Application No. 62/040,821, filed Aug. 22, 2014 and U.S.Provisional Application No. 62/198,433, filed Jul. 29, 2015. Thecontents of these applications are hereby incorporated by reference inits entirety.

BACKGROUND

Cancer is classically thought of as a disease caused by multiple geneticmutations that confer a proliferative and survival advantage toneoplastic cells (1, 2). Extensive investigations have explored the roleof DNA sequence alterations in the pathogenesis of an oncogenicphenotype. Clinically, mutational assessment of pathologic tissue allowsfor definitive diagnosis of multiple tumor types, and specific mutationsthat have previously been shown to correlate with patient prognosis caninform therapeutic decisions (3).

Although the mutational profile of tumor cells is central to tumorpathogenesis and the clinical assessment of patients, it does notencompass the entire biologic dysregulation that occurs in tumor cells.Previous studies have demonstrated that cancer is not only driven byalterations in DNA sequence but also can be driven by epigenetic eventsor disrupted chromatin structure (4). The epigenetic changes of cancercells can occur at multiple levels, including DNA methylation andhistone modifications. Not surprisingly, large-scale analyses ofepigenetic phenomena in various cancer types have shown clearcorrelations between patterns of epigenetic dysregulation and patientoutcome. For example, results from The Cancer Genome Atlas (TCGA)project indicated that at least two types of glioblastoma could beidentified simply from their level of DNA methylation and that thesesubcategories were significantly distinct in terms of patient outcome(5). This correlation between DNA methylation and clinical prognosis hasbeen observed for numerous other cancers including acute myeloidleukemia (AML), T-cell and B-cell lymphoblastic leukemia, non-small celllung carcinoma, ovarian carcinoma and melanoma (6-13).

Despite the clear relationship between DNA methylation and prognosis,assays that assess patterns of DNA methylation are not commonly used inclinical practice. The reasons underlying this paucity of methylationassays likely involve both the techniques and instrumentation requiredfor DNA methylation analysis. Methods for analyzing DNA methylationtypically utilize bisulfite treatment of DNA, which is not standard in aclinical molecular pathology laboratory and can readily result in sampledegradation (14). Multilocus analysis DNA methylation usually involvesplatforms such as custom-made arrays or high-throughput sequencing,thereby substantially raising the cost of clinical implementation.Therefore, there exists a need in the art for DNA methylation assaysthat utilize techniques and equipment that are commonplace in clinicallaboratories.

SUMMARY

The presently disclosed subject matter provides high-throughput methodsfor performing genomic DNA methylation assessments. The presentlydisclosed subject matter further provides methods for diagnosing asubject with a disease and/or disorder and for determining the prognosisof a subject that has a disease and/or disorder.

In certain embodiments, a method for determining the methylation statusof one or more genomic DNA loci includes obtaining a biological samplefrom a subject; isolating genomic DNA from the biological sample;digesting a first sample of the genomic DNA with amethylation-insensitive restriction enzyme to form a first sample ofgenomic DNA fragments; amplifying the first sample of genomic DNAfragments to generate a first sample of amplified DNA fragments;hybridizing a genomic DNA locus-specific probe to the amplified DNAfragments of the first sample; and quantifying the amplified DNAfragments of the first sample hybridized to the locus-specific probe.

In certain embodiments, the method can further include digesting asecond sample of the genomic DNA with a methylation-sensitiverestriction enzyme to form a second sample of genomic DNA fragments;amplifying the second sample of genomic DNA fragments to generate asecond sample of amplified DNA fragments; hybridizing a genomic DNAlocus-specific probe to the amplified DNA fragments of the secondsample; and quantifying the amplified DNA fragments of the second samplehybridized to the locus-specific probe.

In certain embodiments, the method can further include the ligation oflinkers to the DNA fragments generated by the digestion of the genomicDNA prior to the amplification of the DNA fragments. In certainembodiments, the digestion of the genomic DNA and the ligation of thelinkers to the DNA fragments can occur in a single reaction.

In certain embodiments, the method can include comparing the amount ofamplified DNA fragments in the second sample hybridized to thelocus-specific probe to the amount of amplified DNA fragments in thefirst sample hybridized to the locus-specific probe to determine themethylation status of the genomic DNA locus that corresponds to thelocus-specific probe. In certain embodiments, the quantifying ofamplified DNA fragments can be performed by flow cytometry. In certainembodiments, the comparison of the first and second samples can benormalized using the methylation status of one or more control genomicDNA loci.

The presently disclosed subject matter further provides for diagnosticmethods. In certain embodiments, a diagnostic method of the presentdisclosure can include obtaining a biological sample from the subject;determining the methylation status of one or more genomic DNA loci inone or more cells of the biological sample; and diagnosing the diseaseand/or disorder in the subject, wherein the methylation status of theone or more genomic DNA loci indicates the presence of the diseaseand/or disorder in the subject.

In certain embodiments, a method for diagnosing acute myeloid leukemia(AML) in a subject can include obtaining a biological sample from thesubject; determining the methylation status of one or more genomic DNAloci in one or more cells of the biological sample; and diagnosing AMLin the subject, wherein the methylation status of the one or moregenomic DNA loci indicates the presence of AML in the subject. Incertain embodiments, the one or more genomic DNA loci can include, butare not limited to, Chr 17: 2208021 to 2208391 (MSPI0406S00783415); Chr3: 129274773 to 129275235 (MSPI0406S00196536); Chr 1: 11723172 to11723834 (MSPI0406S00011246); Chr 19: 1924052 to 1924259(MSP10406S00861109); Chr 6: 108615428 to 108615973 (MSP10406S00333894);Chr 16: 30538940 to 30539797 (MSP10406S00754805); Chr 12: 53661106 to53661621 (MSP10406S00613804); Chr 15: 65810129 to 65810776(MSPI0406S00715593); Chr 14: 106354882 to 106355276 (MSPI0406S00698115);Chr 12: 6233715 to 6234255 (MSPI0406S00600078); Chr 20: 11899205 to11899843 (MSP10406S00914183); Chr 15: 50838542 to 50839225(MSPI0406S00710190); Chr 3: 8542436 to 8543339 (MSP10406S00163833); Chr16: 68345197 to 68345691 (MSP10406S00765490); Chr 20: 11898849 to11899205 (MSPI0406S00914182); Chr 20: 11898555 to 11898849(MSPI0406S00914181); Chr 2: 158114266 to 158115184 (MSPI0406S00136939);or combinations thereof.

The presently disclosed subject matter further provides for prognosticmethods. In certain embodiments, a prognostic method of the presentdisclosure can include obtaining a biological sample from the subjectthat has a disease and/or disorder; determining the methylation statusof one or more target genomic DNA loci in one or more cells of thebiological sample; and providing a disease and/or disorder prognosisbased on the methylation status of the one or more genomic DNA loci inthe subject.

In certain embodiments, a method for determining the prognosis of asubject that has AML includes obtaining a biological sample from thesubject; determining the methylation status of one or more targetgenomic DNA loci in one or more cells of the biological sample; andproviding an AML prognosis based on the methylation status of the one ormore genomic DNA loci in the subject. In certain embodiments, the one ormore genomic DNA loci can include, but are not limited to, Chr 17:2208021 to 2208391 (MSPI0406S00783415); Chr 3: 129274773 to 129275235(MSP10406S00196536); Chr 1: 11723172 to 11723834 (MSPI0406S00011246);Chr 19: 1924052 to 1924259 (MSPI0406S00861109); Chr 6: 108615428 to108615973 (MSP10406S00333894); Chr 16: 30538940 to 30539797(MSP10406S00754805); Chr 12: 53661106 to 53661621 (MSP10406S00613804);Chr 15: 65810129 to 65810776 (MSPI0406S00715593); Chr 14: 106354882 to106355276 (MSPI0406S00698115); Chr 12: 6233715 to 6234255(MSPI0406S00600078); Chr 20: 11899205 to 11899843 (MSPI0406S00914183);Chr 15: 50838542 to 50839225 (MSPI0406S00710190); Chr 3: 8542436 to8543339 (MSPI0406S00163833); Chr 16: 68345197 to 68345691(MSPI0406S00765490); Chr 20: 11898849 to 11899205 (MSP10406S00914182);Chr 20: 11898555 to 11898849 (MSP10406S00914181); Chr 2: 158114266 to158115184 (MSPI0406S00136939); or combinations thereof.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1A-B. Comparison of the standard MELP assay to expedited MELP(xMELP). A) Schematic of standard MELP and xMELP. Alterations used inxMELP decrease both hands-on labor and turn-around time by a full day.B) Comparison of methylation levels as measured by MELP and xMELP. DNAfrom 10 primary AML samples was subjected to both MELP and xMELP.Methylation levels at 28 loci, measured as log₂([HpaII]/[MspI])normalized to the average methylation level at three control loci, wasdetermined. Comparable methylation levels were obtained with the twomethods at all loci examined.

FIG. 2A-C. Quality control of xMELP assay. xMELP was performed on 207primary AML samples (UPenn cohort). A) After microsphere flow cytometry,mean MFI (median fluorescence intensity) across 31 loci was determined.Distribution of mean MFI is shown. The relatively large number ofsamples with low MFI indicates assay failure for these samples. B)Comparison of MFI from three control loci and mean MFI for each sampleis shown. MFI of the three control loci is plotted. Colors indicate meanMFI (black: ≦50; medium gray: >50: ≦200; light gray: >200, ≦400; darkgray >400, ≦800; open circle: >800). C) Enlargement of the group ofsamples with low MFI of control loci. The dotted box indicates thosesamples for which the assay likely failed, corresponding to an MFIsignal <100 for each of the control loci.

FIG. 3. Variability of xMELP-determined methylation levels. xMELP wasperformed on the entire primary AML UPenn cohort (n=207) and on 21aliquots of a single sample. Methylation levels, measured bylog₂([HpaII]/[MspI]), for each locus were determined and are shown.Black dots indicate 21 replicates of a single sample, while gray dotsrepresent the ratios for all samples in the UPenn cohort in order toillustrate the range of biological variability. Loci included in thefinal xMELP classifier are in black, loci not in the classifier are inlight gray, normalization loci are in gray.

FIG. 4A-C. Variable selection and random survival forest. A) Acomparison of variable importance (x axis) is shown for 31 loci.Distribution of importance scores from independently trained randomforests on the original data are shown in gray; control distributionsderived from perturbation analysis are shown in white. Loci withoriginal>permuted scores (P<0.05) were retained for the final model. B)17 retained loci from (A). C) Error rate (left) and final variableimportance (right) for 17 loci in the final 1000-tree random survivalforest classifier.

FIG. 5A-E. Variability of the overall methylation score (M-score). A)M-scores for the entire primary AML UPenn cohort (gray dots) and for 21replicates of a single primary AML sample (black dots) are shown.Variability of M-score in the replicates is minimal compared tovariability of M-scores across the entire UPenn cohort. B) M-scores ofsix duplicate samples. Line of unity is shown. C) Effect of Ficollcentrifugation and freezing on M-scores. DNA from five primary AML bonemarrow aspirates was isolated prior to Ficoll centrifugation (fresh noFicoll), after Ficoll centrifugation (fresh), and after both Ficollcentrifugation and cryopreservation in DMSO containing media (frozen).Comparison of M-scores for these samples is shown along with the line ofunity. A single fresh sample failed quality control (FIG. 2), so onlyfour samples are shown for plots that include fresh samples. D) Effectof normal DNA contamination on M-scores. DNA from two primary AMLsamples was diluted with varying amounts DNA from normal peripheralblood. M-scores for each dilution are indicated (closed and opencircles). M-scores of the entire primary AML UPenn cohort (gray dots)are shown as a comparison of the variability across AML samples. E)Robustness analysis of M-score score with random locus perturbation. Forreference, the bottom of the figure shows the range of tertiles ofM-score seen in 70 patients from the UPenn cohort (see FIG. 6).

FIG. 6. Outcome analysis based on M-scores. Using the random foresttrained on the HOVON data set, M-scores were determined for seventyprimary AML samples from the UPenn cohort. Samples were ranked byM-score and divided into tertiles. Overall survival for each tertile isplotted (red, lowest M-score group, n=24; blue, middle M-score group,n=23; green, highest M-score group, n=23; p=0.009, log-rank test).

FIG. 7. Effect of input DNA on M-score. Serial dilutions of genomic DNAwere subjected to the xMELP assay. M-scores of dilutions are shown.Hatched lines indicate samples that failed quality control.

FIG. 8. Distribution of M-score by survival status at 2 years in theUPenn Cohort (n=163, n=3 with unknown survival status at 2 yearsexcluded).

FIG. 9. Kaplan-Meier curves of overall survival in the UPenn cohort.Subgroups are determined by the optimal M-score. Curves for the totalcohort (n=166) are shown.

FIG. 10. Kaplan-Meier curves of overall survival in the UPenn cohort.Subgroups are determined by the optimal M-score. Curves for patients <60years with intermediate cytogenetics (n=82; log-rank P=0.001) are shown.

FIG. 11. Kaplan-Meier curves of overall survival in the E1900 cohort.Subgroups are determined by the optimal M-score (n=383; log-rankP<0.00001).

FIG. 12. M-score distribution for the UPenn and E1900 (ECOG) cohorts.

FIG. 13. Chi-statistic by M-score cutpoint in the UPenn cohort (Optimalcutpoint determined to be 86).

FIG. 14. Kaplan-Meier curves of overall survival in UPenn patients whoachieved complete remission (n=118; log-rank P<0.00001).

FIG. 15A-B. E1900 Cohort: Kaplan Meier curves of overall survival bydaunorubicin dose stratified by low and high M-score. A) Low M-score;n=166, log-rank P=0.328. B) High M-score; n=217, log-rank P=0.001.

FIG. 16. An exemplary M-score cutpoint according to a non-limitingembodiment of the present disclosure.

DETAILED DESCRIPTION

The presently disclosed subject matter provides high-throughput methodsfor performing genomic DNA methylation assessments. The presentlydisclosed subject matter further provides methods for diagnosing asubject with a disease and/or disorder. In addition, the disclosedsubject matter provides methods for determining the prognosis of asubject that has a disease and/or disorder.

Definitions

A “biological sample” or “sample,” as used interchangeably herein,refers to a sample of biological material obtained from a subjectincluding cells in culture, cell supernatants, cell lysates, serum,blood, plasma, bone marrow (biopsy and/or aspirate), biological fluid(e.g., blood, plasma, serum, stool, urine, lymphatic fluid, ascites,ductal lavage, nipple aspirate, saliva, broncho-alveolar lavage, tearsand cerebrospinal fluid), and tissue samples. The source of the samplemay be solid tissue (e.g., from a fresh, frozen, and/or preserved organ,tissue sample, biopsy or aspirate), blood or any blood constituents,bodily fluids (such as, e.g., urine, lymph, cerebral spinal fluid,amniotic fluid, peritoneal fluid, or interstitial fluid) or cells fromthe individual. In certain non-limiting embodiments, the biologicalsample is obtained from a tumor. In certain embodiments, the sample canbe a “clinical sample,” which is a sample derived from a patient. Incertain embodiments, the biological sample can be a peripheral bloodsample from a patient. In certain embodiments, the biological sample canbe a bone marrow sample from a patient.

The term “patient” or “subject,” as used interchangeably herein, refersto any warm-blooded animal, e.g., a human. Non-limiting examples ofnon-human subjects include non-human primates, dogs, cats, mice, rats,guinea pigs, rabbits, fowl, pigs, horses, cows, goats, sheep, etc.

As used herein, the terms “restriction endonucleases” and “restrictionenzymes” refer to bacterial enzymes, each of which cut double-strandedDNA at or near a specific nucleotide sequence. DNA molecules are said tohave “5′ ends” and “3′ ends” because mononucleotides are reacted to makeoligonucleotides in a manner such that the 5′ phosphate of onemononucleotide pentose ring is attached to the 3′ oxygen of its neighborin one direction via a phosphodiester linkage. Therefore, an end of anoligonucleotide is referred to as the “5′ end” if its 5′ phosphate isnot linked to the 3′ oxygen of a mononucleotide pentose ring. An end ofan oligonucleotide is referred to as the “3′ end” if its 3′ oxygen isnot linked to a 5′ phosphate of another mononucleotide pentose ring.

As used interchangeably herein, “methylation status” and “methylationlevel” refer to the presence, absence and/or quantity of methylation ata particular nucleotide, or nucleotides, within a DNA region, e.g.,genomic DNA loci. The methylation status of a particular DNA sequence(e.g., within a genomic DNA loci) can indicate the methylation state ofevery base in the sequence or can indicate the methylation state of asubset of the base pairs (e.g., of cytosines) or the methylation stateof one or more specific restriction enzyme recognition sequences withinthe sequence. In certain embodiments, the methylation status of multiplegenomic loci can be used to determine a “methylation score” (M-score),as described below, for use in the prognostic and diagnostic methods ofthe presently disclosed subject matter.

The term “methylation,” as used herein, refers to the presence of amethyl group added by the action of a DNA methyl transferase enzyme to acytosine base or bases in a region of nucleic acid, e.g., genomic DNA.

The term “isolated” (e.g., isolated genomic DNA) refers to a biologicalcomponent that has been substantially separated or purified away fromother biological components in the cell of the organism in which thecomponent naturally occurs, e.g., other chromosomal andextra-chromosomal DNA and RNA, proteins and organelles. Nucleic acids,e.g., DNA, that have been “isolated” include nucleic acids purified bystandard purification methods.

DNA Methylation Detection

The presently disclosed subject matter provides for high-throughputmethods for assessing the methylation status of one or more genomic DNAloci in a biological sample of a subject. In certain embodiments, themethod of the present disclosure can be used to assess the methylationstatus of two or more, three or more, four or more, five or more, six ormore, seven or more, eight or more, ten or more, twelve or more, fifteenor more, seventeen or more, twenty or more, twenty-five or more orthirty or more genomic loci. In certain embodiments, the method of thepresent disclosure can be used to assess the methylation status of aboutseventeen genomic loci.

The methods of the present disclosure include the detection of themethylation status of certain genomic DNA loci. The analyzed genomic DNAloci can include one or more genomic loci that exhibit differentialmethylation in a biological sample from a subject that has a diseaseand/or disorder compared to a reference sample. In certain embodiments,the reference sample can include a biological sample from a healthysubject or a healthy and/or unaffected biological sample from a subjectthat has the disease and/or disorder.

DNA Methylation Detection by Microsphere HELP (MELP)

In certain embodiments, the MELP method for determining the methylationstatus of a genomic DNA locus includes obtaining genomic DNA from abiological sample from a subject. The genomic DNA can be isolated fromthe biological sample by any method known in the art. For example, andnot by way of limitation, genomic DNA can be isolated from a biologicalsample by using the phenol-chloroform DNA extraction method.Commercially available kits can also be conveniently used for thispurpose in accordance with the instructions provided by theirmanufacturers, such kits are available from the following manufacturers:Invitrogen, San Diego, Calif.; Stratagene, La Jolla, Calif. In certainembodiments, genomic DNA can be isolated from a biological sample usingcommercially available kits (e.g., PureLink® Genomic DNA kit from LifeTechnologies).

The method for analyzing the methylation status of one or more genomicDNA loci can further include fragmenting the genomic DNA by digestingthe DNA with one or more restriction enzymes. In certain embodiments,the genomic DNA can be digested by a methylation-insensitive and/ormethylation-sensitive restriction enzyme. A methylation-sensitiverestriction enzyme is a restriction enzyme that cuts DNA if itsrecognition sequence is unmethylated. A methylation-insensitive enzymeis a restriction enzyme that cuts DNA independent of the methylationstatus of its recognition sequence, i.e., the enzyme will cut therestriction site if it is methylated or unmethylated. Non-limitingexamples of methylation-sensitive restriction enzymes include AatII,AcciI, AcII, Aor13HI, AgeI-HF®, AscI, AsiSI, AvaI, BspT104I, BssHII,DpnII, EagI, HaeII, HgaI, HhaI, HpaII, KasI, MluCI, NotI, NruI, Sau3AI,SalI-HF®, ScrFI, SfoI, SmaI, SnaBI and ZraI.

In certain embodiments, the methylation-insensitive and themethylation-sensitive restriction enzyme used in the disclosed methodrecognize the same restriction site. In certain embodiments, therestriction enzyme recognition site is 5′-CCGG-3′. For example, and notby way of limitation, the methylation-insensitive restriction enzyme canbe MspI and/or the methylation-sensitive restriction enzyme can beHpaII. The presence of methylated nucleotides (e.g., cytosines) in thegenome can result in the methylation-sensitive restriction enzymegenerating fewer and longer fragments than the methylation-insensitiverestriction enzyme, thereby allowing analysis of the methylation statusof various genomic DNA loci by comparison of the quantity of thefragments received by MspI digestion to the quantity of fragmentsreceived by HpaII digestion for a particular genomic DNA loci.

The genomic DNA fragments generated by the contact of the isolatedgenomic DNA with a methylation-insensitive and/or methylation-sensitiverestriction enzyme can be ligated to DNA linkers to allow foramplification of the genomic DNA fragments. In certain embodiments, thelinkers can be oligonucleotides of sufficient length to hybridize to theDNA fragments. For example, and not by way of limitation, the length ofeach linker can be greater than about 10 nucleotides, greater than about15 nucleotides, greater than about 20 nucleotides or greater than about25 nucleotides. In certain embodiments, the linker can include a primersite for PCR amplification of the DNA fragments. In certain embodiments,the linkers for use in the present disclosure can include doublestranded DNA that has 5′ overhangs. In certain embodiments, the methodincludes purification of the DNA fragments from the restriction enzymesused in the digestion step followed by ligation of the linkers to theDNA fragments.

The methods for analyzing the methylation status of one or more genomicDNA loci can further include amplification of the DNA fragments.Amplification of the DNA fragments can be performed by any method knownin the art. For example, and not by way of limitation, polymerase chainreaction (PCR) can be performed to amplify the genomic DNA fragments.PCR can include annealing nucleic acid primers to a complementary targetDNA strand by nucleic acid hybridization to form a hybrid between theprimer and the target DNA strand, and then extend the primer along thetarget DNA strand by a DNA polymerase enzyme, e.g., Taq Polymerase.Alternatively and/or additionally, the linkers ligated to the DNAfragments, as discussed above, can serve as primers for the PCRreaction. For example, the JHpaII 24 nucleic acid strand of thedouble-stranded linkers formed by the annealing of the JHpaII 12 primerand the JHpaII 24 primer can serve as the primer for the PCR reaction.

The primers used in the methods of the disclosure can be, for example,DNA oligonucleotides having 10 nucleotides or more in length. In certainembodiments, the primers can include DNA oligonucleotides that are about15, about 20, about 25, about 30 or about 50 nucleotides or more inlength. In certain embodiments, the primer for use in the PCR reactioncan include JHpaII 24, disclosed below. The oligonucleotide primers ofthe invention can be prepared using any suitable method, such asconventional phosphotriester and phosphodiester methods or automatedembodiments thereof. The primers can be added to the amplificationmethod in any amount effective for amplifying the target nucleic acidpresent in the sample.

Non-limiting examples of primers and/or linkers that can be used in themethods of the disclosed subject matter are as follows:

(SEQ ID NO: 1) JHpaII 12: 5′-CGGCTGTTCATG-3′ (SEQ ID NO: 2)JHpaII 24: 5′-CGACGTCGACTATCCATGAACAGC-3′

For example, the JHpaII 12 primer and the JHpaII 24 primer can beannealed to serve as the linkers described above.

The method can further include quantifying the amount of amplified DNAfragments, e.g., PCR amplicons. In certain embodiments, quantificationof the PCR amplicons can include hybridizing the PCR amplicons tomicrospheres, fluorescently labeling the amplicons and performing flowcytometry. The methods of the disclosed subject matter do not includethe use of microarrays to quantify the PCR amplicons. In certainembodiments, the microspheres can be about 5 microns in size. In certainembodiments, the microspheres can be made of polystyrene. A non-limitingexample of microspheres that can be used in the presently disclosedsubject matter is MicroPlex® microspheres from Luminex Corp. (Austin,Tex.). For example, and not by way of limitation, the QuantiGene Plex2.0 Assay (Affymetrix, Santa Clara, Calif.) can be used to quantify thePCR amplicons. In certain embodiments, the PCR amplicons can behybridized to locus-specific oligonucleotide probes to form PCRamplicon/locus-specific probe complexes followed by hybridization of thecomplexes to the microspheres. In certain embodiments, thelocus-specific oligonucleotide probes can be conjugated to themicrospheres followed by hybridization of the probe with the PCRamplicon to form PCR amplicon/locus-specific probe complexes. Thenucleic acid sequence of the locus-specific probe can correspond to thesequence of the genomic locus that is being analyzed by the disclosedDNA methylation detection method.

The amplicon/locus-specific probe complexes hybridized to themicrospheres can be fluorescently labeled. In certain embodiments, thelocus-specific probe can be labeled directly, e.g., conjugated, orindirectly to a detectable label. Non-limiting examples of detectablelabels include fluorescent labels (e.g., fluorophores), radiolabels(e.g., ³H, ¹²⁵I, ³⁵S, ¹⁴C, ³²P or ³³P), enzymes (e.g., LacZ, horseradishperoxidase, alkaline phosphatase) and nucleic acid intercalators (e.g.,ethidium bromide). Non-limiting examples of fluorophores includerhodamine, fluorescein, green fluorescent protein, luciferase, Cy3, Cy5,phycoerythrin or ROX. Alternatively or additionally, fluorescentlabeling of the amplicon/locus-specific probe complexes can be performedby hybridization of the complexes to a label oligonucleotide probe. Forexample, and not by way of limitation, the locus-specific probe caninclude biotinylated nucleotides that bind to a fluorophore-conjugatedstreptavidin compound. In certain embodiments, thefluorophore-conjugated streptavidin compound can bephycoerythrin-streptavidin.

In certain embodiments, the methylation status and/or level of anindividual genomic locus can be determined by the comparing the amountof amplified DNA fragments (e.g., PCR amplicons) in the digest of themethylation-insensitive restriction enzyme for a particular locus to theamount of DNA fragments (e.g., PCR amplicons) in the digest of themethylation-sensitive restriction enzyme for the same particular locus(e.g., identified during the detection method by the use of thelocus-specific probe disclosed above). The comparison can be normalizedto the methylation level of one or more control genomic DNA loci. Ifmore than one control genomic locus is used for normalization, theaverage methylation status of the two or more genomic loci can be usedfor normalization. In certain embodiments, the control genomic loci areloci that are typically unmethylated in the biological sample being usedin the disclosed methods.

For example, and not by way of limitation, the methylation level can bedetermined using the following formula:

log₂(HpaII/MspI)  [Formula 1]

wherein, the result of Formula I can be normalized to the methylationlevel of the control genomic DNA loci analyzed.

In certain embodiments, the methylation levels of two or more genomicDNA loci can be represented in a methylation outcome score (M-score orMS). In certain embodiments, the M-score is determined by the followingformula:

MS=a(L1)+b(L2)+c(L3)+d(L4)  [Formula 2]

where L# is the methylation level (i.e., normalized HpaII/MspI ratio) ateach locus, and the associated constant (a, b, c) is the weightingfactor, as determined by a training algorithm (SuperPC;http://statweb.stanford.edu/wtibs/superpc).

The methods of the present disclosure include the detection of themethylation status of certain genomic DNA loci. The analyzed genomic DNAloci can include one or more genomic loci that exhibit differentialmethylation in a biological sample from a subject that has a diseaseand/or disorder compared to a reference sample. In certain embodiments,the reference sample can include a biological sample from a healthysubject or a healthy and/or unaffected biological sample from a subjectthat has the disease and/or disorder.

DNA Methylation Detection by Expedited MELP (xMELP)

In certain embodiments, the xMELP method for determining the methylationstatus of a genomic DNA locus includes obtaining genomic DNA from abiological sample from a subject, as disclosed above. The method canfurther include fragmenting the genomic DNA by digesting the DNA withone or more restriction enzymes. In certain embodiments, the genomic DNAcan be digested by a methylation-insensitive and/ormethylation-sensitive restriction enzyme, described above. In certainembodiments, the methylation-insensitive restriction enzyme can includeMspI and/or the methylation-sensitive restriction enzyme can includeHpaII. The genomic DNA fragments generated by the contact of theisolated genomic DNA with a methylation-insensitive and/ormethylation-sensitive restriction enzyme can be ligated to nucleic acidlinkers to allow amplification of the genomic DNA fragments, asdisclosed above.

In certain embodiments, the linkers used in the disclosed method can beannealed pairs of nucleic acid primers. Non-limiting examples of primersand/or linkers that can be used or modified to be used in the methods ofthe disclosed subject matter are as follows:

(SEQ ID NO: 3) JHpaII 12XXXX: 5′-CGCCTGTTCATG-3′ (SEQ ID NO: 4)JHpaII 24XXXX: 5′-CGACGTCGACTATCCATGAACAGG-3′

For example, the JHpaII 12XXXX primer and the JHpaII 24XXXX primer canbe annealed to serve as the linkers described above.

Amplification of the DNA fragments can be performed by any method knownin the art. In certain embodiments, PCR can be performed to amplify thegenomic DNA fragments using nucleic acid primers described above. Theprimers used in the methods of the disclosure can be, for example, DNAoligonucleotides having 10 nucleotides or more in length. In certainembodiments, the primers can include DNA oligonucleotides that are about15, about 20, about 25, about 30 or about 50 nucleotides or more inlength. In certain embodiments, the primer for use in the PCR reactioncan include JHpaII 24XXXX, disclosed below. Alternatively and/oradditionally, the linkers ligated to the DNA fragments can serve asprimers for the PCR reaction. For example, the JHpaII 24XXXX nucleicacid strand of the double-stranded linkers formed by the annealing ofthe JHpaII 12XXXX primer and the JHpaII 24XXXX primer can serve as theprimer for the PCR reaction.

In certain embodiments, the digestion of the genomic DNA by restrictionenzymes and the ligation of the DNA fragments to the linkers can beperformed in a single reaction (see FIG. 1A). In comparison to the MELPDNA methylation detection method disclosed above, the nucleic acidsequences of the JHpaII 12XXXX primer and the JHpaII 24XXXX primerallows the performance of the digestion step and ligation step into asingle reaction. For example, in the MELP method, the use of annealedJHpaII 12 primers and the JHpaII 24 primers, as the linkers in the MELPassay, results in the formation of a HpaII/MspI restriction site duringthe ligation step. The presence of this restriction site requires thepurification of the genomic DNA fragments to remove the restrictionenzymes used in the digestion step from the sample of DNA fragmentsprior to the ligating of the linkers to the DNA fragments. In contrast,the JHpaII 12XXXX primer and the JHpaII 24XXXX primer, when annealed andused as linkers, do not form a HpaII/MspI restriction site and allowsthe exclusion of the intermediate DNA purification step from between thedigestion and ligation steps, thereby permitting the combination of thedigestion step and ligation step into a single reaction.

The combination of the digestion and ligation reaction in a singlereaction reduces the amount of time needed to perform thehigh-throughput DNA methylation assessment, and allows rapid turn-aroundtime of the results of the DNA methylation analysis in a clinicalsetting. For example, and not by way of limitation, the xMELP DNAmethylation assessment method disclosed herein can be performed withinabout 6 hours to about 48 hours or within about 12 hours to about 48hours. The timeframe of the xMELP DNA methylation assessment method issignificantly shorter than the MELP DNA methylation assessment method,disclosed above, which can take from about 60 to about 72 hours tocomplete.

In certain embodiments, the single digestion and ligation reaction caninclude genomic DNA, annealed oligonucleotide primers, ATP, DNA ligase,a restriction enzyme, e.g., MspI or HpaII, and the appropriate buffersfor the ligase and restriction enzymes. In certain embodiments, thesingle digestion and ligation reaction can be carried out at atemperature of about 25° C. for about 12 hours.

The method can further include quantifying the amount of amplified DNAfragments, e.g., PCR amplicons. In certain embodiments, quantificationof the PCR amplicons can include hybridizing the PCR amplicons tomicrospheres, fluorescently labeling the amplicons and performing flowcytometry, as disclosed above. In certain embodiments, the methylationstatus (e.g., level) of each individual genomic locus analyzed can thenbe determined by the comparing the amount of DNA fragments (e.g., PCRamplicons) in the digest of the methylation-insensitive restrictionenzyme for a particular locus to the amount of DNA fragments in thedigest of the methylation-sensitive restriction enzyme for the sameparticular locus (e.g., PCR amplicons), normalized to the methylationlevel of the control genomic DNA loci. As discussed above, themethylation level can be determined using Formula 1.

In certain embodiments, the xMELP method for assessment of genomic DNAmethylation can analyze the methylation status of individual genomic DNAloci in small quantities of genomic DNA that has been isolated from abiological sample. For example, and not by way of limitation, the xMELPmethod can be used to perform DNA methylation assessment on samples thatcontain low levels of genomic DNA, such as about 2 ng of genomic DNA.

In comparison to the MELP assay disclosed above, the xMELP assayprovides a more uniform quality control metric. For example, and not byway of limitation, analysis of the methylation level of multiple genomicloci allowed the determination of a quality control cutoff. In certainembodiments, the quality control cutoff can be the control loci value<100.

DNA Methylation Detection Method for Acute Myeloid Leukemia (AML)

The present disclosure provides high-throughput methods for identifyingAML in a biological sample of a subject. The high-throughput methodincludes determining the methylation status of one or more genomic DNAloci in the biological sample using a DNA methylation detection methoddisclosed herein.

In certain embodiments, the one or more genomic DNA loci can include,but are not limited to, Chr 17: 2208021 to 2208391 (MSPI0406S00783415);Chr 20: 32274469 to 32275009 (MSP10406S00920592); Chr 6: 3024925 to3025589 (MSP10406S00304798); Chr 3: 129274773 to 129275235(MSPI0406S00196536); Chr 14: 105860849 to 105861218 (MSPI0406S00697563);Chr 1: 11723172 to 11723834 (MSPI0406S00011246); Chr 19: 1924052 to1924259 (MSPI0406S00861109); Chr 6: 108615428 to 108615973(MSPI0406S00333894); Chr 16: 30538940 to 30539797 (MSP10406S00754805);Chr 12: 53661106 to 53661621 (MSPI0406S00613804); Chr 3: 48601900 to48602237 (MSP10406S00176846); Chr 15: 65810129 to 65810776(MSP10406S00715593); Chr 14: 106354882 to 106355276 (MSPI0406S00698115);Chr 12: 6233715 to 6234255 (MSP10406S00600078); Chr 20: 11899205 to11899843 (MSPI0406S00914183); Chr 15: 50838542 to 50839225(MSP10406S00710190); Chr 3: 8542436 to 8543339 (MSPI0406S00163833); Chr16: 68345197 to 68345691 (MSPI0406S00765490); Chr 20: 11898849 to11899205 (MSP10406S00914182); Chr 20: 11898555 to 11898849(MSP10406S00914181); Chr X: 48795887 to 48797005 (MSPI0406S00997890);Chr 18: 5293969 to 5294770 (MSPI0406S00838340); Chr 2: 158114266 to158115184 (MSPI0406S00136939); Chr 14: 24867489 to 24867729(MSPI0406S00669709); Chr 1: 32739167 to 32739750 (MSPI0406S00027418);Chr 11: 118763110 to 118763426 (MSP10406S00589152); Chr 20: 814970 to815202 (MSPI0406S00910305); Chr 15: 45003463 to 45004002(MSPI0406S00708912); or combinations thereof.

In certain embodiments, the one or more genomic DNA loci can include,but are not limited to Chr 17: 2208021 to 2208391 (MSP10406S00783415);Chr 3: 129274773 to 129275235 (MSP10406S00196536); Chr 1: 11723172 to11723834 (MSPI0406S00011246); Chr 19: 1924052 to 1924259(MSP10406S00861109); Chr 6: 108615428 to 108615973 (MSP10406S00333894);Chr 16: 30538940 to 30539797 (MSPI0406S00754805); Chr 12: 53661106 to53661621 (MSP10406S00613804); Chr 15: 65810129 to 65810776(MSPI0406S00715593); Chr 14: 106354882 to 106355276 (MSPI0406S00698115);Chr 12: 6233715 to 6234255 (MSP10406S00600078); Chr 20: 11899205 to11899843 (MSPI0406S00914183); Chr 15: 50838542 to 50839225(MSPI0406S00710190); Chr 3: 8542436 to 8543339 (MSPI0406S00163833); Chr16: 68345197 to 68345691 (MSPI0406S00765490); Chr 20: 11898849 to11899205 (MSP10406S00914182); Chr 20: 11898555 to 11898849(MSPI0406S00914181); Chr 2: 158114266 to 158115184 (MSP10406S00136939);or combinations thereof.

In certain embodiments, the one or more genomic DNA loci can include oneor more of the genomic loci listed in Tables 1 and 2.

In certain embodiments, the method can further include detecting themethylation status of one or more genomic DNA loci as a control. Forexample, and not by way of limitation, the one or more control genomicDNA loci can include Chr 6: 34856156 to 34857019 (MSPI0406S00318682);Chr 13: 53028642 to 53029495 (MSPI0406S00653944); Chr 19: 37958559 to37958860 (MSPI0406S00890278); or combinations thereof.

Diagnostic, Prognostic and Therapeutic Methods

The present disclosure provides diagnostic and prognostic methods fordiseases and/or disorders that are characterized by differentialmethylation of genomic sequences, e.g., differential methylation ofcytosines (CpG dinucleotide sequences).

In certain embodiments, the present disclosure provides diagnosticmethods for determining the presence of a disease and/or disorder in asubject by assessing the DNA methylation profiles characteristicallyassociated with the disease and/or disorder. For example, and not by wayof limitation, the diagnostic method can include (a) obtaining abiological sample from the subject; (b) determining the methylationstatus of one or more genomic DNA loci in one or more cells of thebiological sample; and (c) diagnosing the disease and/or disorder in thesubject, wherein the methylation status of the one or more genomic DNAloci indicates the presence of the disease and/or disorder in thesubject. In certain embodiments, the diagnosis can be based on amethylation score (M-score) that is derived from the methylation statusof all the target genomic DNA loci analyzed. In certain embodiments, thepresently disclosed subject matter can be used for the diagnosis and/orprognosis of diseases and/or disorders such as, but not limited to,cancer, autoimmune diseases, coronary artery disease and aging.

In certain embodiments, the present disclosure provides diagnosticmethods for determining the presence of cancer in a subject by assessingthe DNA methylation profiles characteristically associated with thecancer. For example, and not by way of limitation, the diagnostic methodcan include (a) obtaining a biological sample from the subject; (b)determining the methylation status of one or more genomic DNA loci inone or more cells of the biological sample; and (c) diagnosing cancer inthe subject, wherein the methylation status of the one or more genomicDNA loci indicates the presence of cancer in the subject. In certainembodiments, the presently disclosed subject matter can be used for thediagnosis and/or prognosis of cancers such as, but not limited to,melanoma, non-small cell lung cancer, glioblastoma, ovarian cancer,leukemia and lymphoblastic leukemia.

In certain embodiments, the present disclosure provides prognosticmethods for determining the prognosis of a subject that has a diseaseand/or disorder by assessing the DNA methylation profilescharacteristically associated with the disease and/or disorder. Forexample, and not by way of limitation, the prognostic method can include(a) obtaining a biological sample from the subject; (b) determining themethylation status of one or more target genomic DNA loci in one or morecells of the biological sample; and (c) providing a disease and/ordisorder prognosis based on the methylation status of the one or moregenomic DNA loci in the subject. In certain embodiments, the prognosiscan be based on a methylation score (M-score) that is derived from themethylation status of all the target genomic DNA loci analyzed.

In certain embodiments, the present disclosure provides prognosticmethods for determining the prognosis of a subject that has cancer byassessing the DNA methylation profiles characteristically associatedwith the cancer. For example, and not by way of limitation, theprognostic method can include (a) obtaining a biological sample from thesubject; (b) determining the methylation status of one or more targetgenomic DNA loci in one or more cells of the biological sample; and (c)providing a cancer prognosis based on the methylation status of the oneor more genomic DNA loci in the subject.

In certain embodiments, the methods for detection of the methylationstatus of one or more target genomic DNA loci can be used to monitor theresponse in a subject to prophylactic or therapeutic treatment (forexample, chemotherapy to reduce tumor cell growth and/or metastasis). Incertain non-limiting embodiments, the disclosed subject matter furtherprovides a method of treatment including measuring the methylationstatus of one or more target genomic DNA loci in a biological sample ofa subject at a first timepoint, administering a therapeutic agent,re-measuring the methylation status of the one or more target genomicDNA loci at a second timepoint, comparing the results of the first andsecond measurements and optionally modifying the treatment regimen basedon the comparison.

In certain embodiments, the first timepoint is prior to anadministration of the therapeutic agent, and the second timepoint isafter said administration of the therapeutic agent. In certainembodiments, the first timepoint is prior to the administration of thetherapeutic agent to the subject for the first time. In certainembodiments, the dose (defined as the quantity of therapeutic agentadministered at any one administration) is increased or decreased inresponse to the comparison. In certain embodiments, the dosing interval(defined as the time between successive administrations) can beincreased or decreased in response to the comparison, including totaldiscontinuation of treatment. In addition, the method of the presentdisclosure can be used to determine the efficacy of the therapeutictreatment, wherein a change in the methylation status of certain genomicDNA loci in a biological sample of a subject can indicate that thetherapeutic treatment regimen can be reduced or stopped.

In certain embodiments, the information provided by the methodsdescribed herein can be used by a physician in determining the mosteffective course of treatment (e.g., preventative or therapeutic) forthe subject. A course of treatment refers to the measures taken for apatient after the prognosis or the assessment of increased risk fordevelopment of a disease and/or disorder is made. For example, when asubject is identified to have an increased risk of developing cancer,the physician can determine whether frequent monitoring for DNAmethylation changes can be performed as a prophylactic measure. Also,when a subject is diagnosed with cancer (e.g., based on the presence ofa DNA methylation pattern in a sample from a subject), it can beadvantageous to follow such detection with a biopsy, surgical treatment,chemotherapy, radiation, immunotherapy, biological modifier therapy,gene therapy, vaccines and the like, or adjust the span of time duringwhich the patient is treated.

The presently disclosed subject matter further provides assays and/ormethods for determining the DNA methylation status of target genomicloci that correlates with the presence, absence and/or severity of adisease and/or disorder. In certain embodiments, a method can includecomparing the methylation status of genomic DNA loci in a biologicalsample from a subject that has a disease and/or disorder to themethylation status of genomic DNA loci in a biological sample from ahealthy subject to determine the methylation pattern, as disclosedabove, that correlates with the presence of the disease and/or disorder.In certain embodiments, a method can include comparing the methylationstatus of genomic DNA loci in a biological sample from a subject thathas a disease and/or disorder at an early stage to the methylationstatus of genomic DNA loci in a biological sample from a subject thathas the disease and/or disorder at a late stage, as disclosed above, todetermine the methylation pattern that correlates with the differentstages of the disease and/or disorder.

Prognostic, Therapeutic and Diagnostic Methods for AML

The presently disclosed subject matter provides diagnostic andprognostic methods for AML that includes determining the methylationstatus of one or more genomic DNA loci in a biological sample of asubject.

In certain embodiments, a method for diagnosing AML in a subjectincludes (a) obtaining a biological sample from the subject; (b)determining the methylation status of one or more genomic DNA loci inone or more cells of the biological sample; and (c) diagnosing AML inthe subject, wherein the methylation status of the one or more genomicDNA loci indicates the presence of AML in the subject.

In certain embodiments, a prognostic method for determining theprognosis of a subject that has AML includes (a) obtaining a biologicalsample from the subject; (b) determining the methylation status of oneor more target genomic DNA loci in one or more cells of the biologicalsample; and (c) providing an AML prognosis based on the methylationstatus of the one or more genomic DNA loci in the subject.

In certain embodiments, the diagnosis and/or prognosis can be based on amethylation score (M-score) that is derived from the methylation statusof one or more of the target genomic DNA loci analyzed. In certainembodiments, a lower M-score indicates a better AML prognosis. Forexample, and not by way of limitation, an M-score less than about 90,less than about 89, less than about 88, less than about 87, less thanabout 86, less than about 85, less than about 84, less than about 83,less than about 82, less than about 81 or less than about 80 indicates abetter AML prognosis than a higher M-score. In certain embodiments, anM-score greater than about 91, greater than about 92, greater than about93, greater than about 94, greater than about 95, greater than about 96,greater than about 97, greater than about 98, greater than about 99 orgreater than about 100 indicates a worse AML prognosis than a lowerM-score. In certain embodiments, an M-score less than about 90, lessthan about 89, less than about 88, less than about 87, less than about86, less than about 85, less than about 84, less than about 83, lessthan about 82, less than about 81 or less than about 80 indicates abetter AML prognosis than an M-score greater than about 91, greater thanabout 92, greater than about 93, greater than about 94, greater thanabout 95, greater than about 96, greater than about 97, greater thanabout 98, greater than about 99 or greater than about 100. In certainembodiments, an M-score less than about 86 indicates a better AMLprognosis than an M-score greater than or equal to about 86.

In certain embodiments, an M-score less than or equal to about 86indicates a better AML prognosis than an M-score greater than about 86.In certain embodiments, an M-score less than about 89 indicates a betterAML prognosis than an M-score greater than or equal to about 89. Incertain embodiments, an M-score less than or equal to about 89 indicatesa better AML prognosis than an M-score greater than about 89.

In certain embodiments, the diagnosis and/or prognostic method for AMLcan include the analysis of the methylation status of two or more, threeor more, four or more, five or more, six or more, seven or more, eightor more, ten or more, twelve or more, fifteen or more, seventeen ormore, twenty or more, twenty-five or more or thirty or more genomicloci. In certain embodiments, the analysis of the methylation status ofthe two or more genomic loci can be performed simultaneously. In certainembodiments, the diagnosis and/or prognostic method for AML can includethe analysis of the methylation status of seventeen different genomicloci.

The detection of the one or more genomic loci can be performing usingthe DNA methylation detection methods disclosed herein, or can beperformed using any of the DNA detection methods known in the art. Incertain embodiments, the diagnostic and/or prognostic methods caninclude determining the methylation status of one or more genomic DNAloci by the DNA methylation detection method MELP, disclosed above. Incertain embodiments, the diagnostic and/or prognostic methods caninclude determining the methylation status of one or more genomic DNAloci by the DNA methylation detection method xMELP, disclosed above.

In certain embodiments, the one or more genomic DNA loci can include,but are not limited to, Chr 17: 2208021 to 2208391 (MSPI0406S00783415);Chr 20: 32274469 to 32275009 (MSP10406S00920592); Chr 6: 3024925 to3025589 (MSPI0406S00304798); Chr 3: 129274773 to 129275235(MSP10406S00196536); Chr 14: 105860849 to 105861218 (MSP10406S00697563);Chr 1: 11723172 to 11723834 (MSPI0406S00011246); Chr 19: 1924052 to1924259 (MSP10406S00861109); Chr 6: 108615428 to 108615973(MSP10406S00333894); Chr 16: 30538940 to 30539797 (MSP10406S00754805);Chr 12: 53661106 to 53661621 (MSP10406S00613804); Chr 3: 48601900 to48602237 (MSP10406S00176846); Chr 15: 65810129 to 65810776(MSP10406S00715593); Chr 14: 106354882 to 106355276 (MSPI0406S00698115);Chr 12: 6233715 to 6234255 (MSPI0406S00600078); Chr 20: 11899205 to11899843 (MSP10406S00914183); Chr 15: 50838542 to 50839225(MSPI0406S00710190); Chr 3: 8542436 to 8543339 (MSPI0406S00163833); Chr16: 68345197 to 68345691 (MSP10406S00765490); Chr 20: 11898849 to11899205 (MSP10406S00914182); Chr 20: 11898555 to 11898849(MSPI0406S00914181); Chr X: 48795887 to 48797005 (MSP10406S00997890);Chr 18: 5293969 to 5294770 (MSPI0406S00838340); Chr 2: 158114266 to158115184 (MSPI0406S00136939); Chr 14: 24867489 to 24867729(MSPI0406S00669709); Chr 1:32739167 to 32739750 (MSP10406S00027418); Chr11: 118763110 to 118763426 (MSP10406S00589152); Chr 20: 814970 to 815202(MSPI0406S00910305); Chr 15: 45003463 to 45004002 (MSPI0406S00708912);or combinations thereof.

In certain embodiments, the one or more genomic DNA loci can include,but are not limited to Chr 17: 2208021 to 2208391 (MSPI0406S00783415);Chr 3: 129274773 to 129275235 (MSP10406S00196536); Chr 1: 11723172 to11723834 (MSPI0406S00011246); Chr 19: 1924052 to 1924259(MSP10406S00861109); Chr 6: 108615428 to 108615973 (MSPI0406S00333894);Chr 16: 30538940 to 30539797 (MSP10406S00754805); Chr 12: 53661106 to53661621 (MSPI0406S00613804); Chr 15: 65810129 to 65810776(MSPI0406S00715593); Chr 14: 106354882 to 106355276 (MSPI0406S00698115);Chr 12: 6233715 to 6234255 (MSPI0406S00600078); Chr 20: 11899205 to11899843 (MSP10406S00914183); Chr 15: 50838542 to 50839225(MSP10406S00710190); Chr 3: 8542436 to 8543339 (MSPI0406S00163833); Chr16: 68345197 to 68345691 (MSP10406S00765490); Chr 20: 11898849 to11899205 (MSP10406S00914182); Chr 20: 11898555 to 11898849(MSPI0406S00914181); Chr 2: 158114266 to 158115184 (MSP10406S00136939);or combinations thereof.

In certain embodiments, the one or more genomic DNA loci can include oneor more of the genomic loci listed in Tables 1 and 2.

In certain embodiments, the method can further include detecting themethylation status of genomic DNA loci as a control. For example, andnot by way of limitation, control genomic DNA loci can include Chr 6:34856156 to 34857019 (MSPI0406S00318682); Chr 13: 53028642 to 53029495(MSPI0406S00653944); Chr 19: 37958559 to 37958860 (MSPI0406S00890278);or combinations thereof.

In certain embodiments, the method can further include determiningand/or evaluating additional prognostic criteria. For example, and notby way of limitation, additional prognostic criteria can include whiteblood cell (WBC) count, age, sex, cytogenetic risk, complete remission(CR) status, minimal residual disease (MRD) and the mutational status ofgenes such as DNMT3A, IDH1, FLT3-ITD and/or NPM1. Additionalnon-limiting examples of genes that can provide prognostic informationby analysis of their mutational status include ASXL1, ATM, BRAF, CBL,DNMT3A, ETV6, EZH2, IDH1, IDH2, JAK2, KIT, KLHL6, KRAS, NRAS, PTEN,PTPN11, PHF6, RUNX1, SF3B1, TET2, TP53 and WT1. In certain embodiments,these prognostic criteria can be combined with the M-score to determinethe prognosis of a subject having AML. For example, and not by way oflimitation, the M-score can be combined with the cytogenetic risk of thepatient to generate a multivariable prognostic model. In certainembodiments, the M-score can be combined with age and/or the mutationalstatus of genes, disclosed herein, such as DNMT3A, IDH1, FLT3-ITD and/orNMP1 to generate a multivariable prognostic model for determining theprognosis of a subject that has AML.

In certain embodiments, the methods for detection of the methylationstatus of one or more target genomic DNA loci can be used to monitor theresponse in a subject that has AML to prophylactic or therapeutictreatment. In certain embodiments, the disclosed subject matter furtherprovides a method of treating AML that can include measuring themethylation status of one or more target genomic DNA loci in abiological sample of a subject at a first timepoint, administering atherapeutic agent, re-measuring the methylation status of the one ormore target genomic DNA loci at a second timepoint, comparing theresults of the first and second measurements and optionally modifyingthe treatment regimen based on the comparison.

In certain embodiments, the information provided by the methodsdescribed herein can be used by a physician in determining the mosteffective course of treatment (e.g., preventative or therapeutic) forthe subject, e.g., to produce an anti-cancer effect. An “anti-cancereffect” refers to one or more of a reduction in aggregate cancer cellmass, a reduction in cancer cell growth rate, a reduction in cancer cellproliferation, a reduction in tumor mass, a reduction in tumor volume, areduction in tumor cell proliferation, a reduction in tumor growth rate,and/or a reduction in tumor metastasis. In certain embodiments, ananti-cancer effect can refer to remission, a complete response, apartial response, a stable disease (without progression or relapse), aresponse with a later relapse or progression-free survival in a patientdiagnosed with cancer.

In certain embodiments, the treatment of a subject that has AML can beinformed by the M-score obtained from the disclosed methods. Forexample, and not by way of limitation, if a subject that has AML isdetermined to have a high M-score (e.g., an M-score greater than about86) by using the methods disclosed herein, high-doses of achemotherapeutic agent (e.g., daunorubicin administered at aconcentration greater than or equal to about 90 mg/m² daily (e.g., forabout 3 days)) can be predicted to have superior anti-cancer effect inthe subject than lower doses of a chemotherapeutic agent (e.g.,daunorubicin administered at a concentration less than or equal to about45 mg/m² daily (e.g., for about 3 days)). In certain embodiments, if asubject that has AML is determined to have a high M-score by using themethods disclosed herein, two cycles of induction chemotherapy (i.e.,chemotherapy given to induce remission) will likely have an anti-cancereffect in the subject. For example, and not by way of limitation, theinduction chemotherapy regimen can include anthracycline and/orcytarabine.

Reports, Programmed Computers and Systems

The results of a test (e.g., a subject's DNA methylation score and/orthe methylation status of individual genomic DNA loci), based onperforming the disclosed methods, and/or any other informationpertaining to a test, can be referred to herein as a “report.” Atangible report can optionally be generated as part of a testing process(which can be interchangeably referred to herein as “reporting,” or as“providing” a report, “producing” a report or “generating” a report).

Examples of tangible reports can include, but are not limited to,reports in paper (such as computer-generated printouts of test results)or equivalent formats and reports stored on computer readable medium(such as a CD, USB flash drive or other removable storage device,computer hard drive, or computer network server, etc.). Reports,particularly those stored on computer readable medium, can be part of adatabase, which can optionally be accessible via the internet (such as adatabase of patient records or genetic information stored on a computernetwork server, which can be a “secure database” that has securityfeatures that limit access to the report, such as to allow only thepatient and the patient's medical practitioners to view the report whilepreventing other unauthorized individuals from viewing the report, forexample). In addition to, or as an alternative to, generating a tangiblereport, reports can also be displayed on a computer screen (or thedisplay of another electronic device or instrument).

A report can include, for example, an individual's diagnosis, such asAML, or can just include a patient's DNA methylation results (forexample, a report on computer readable medium such as a network servercan include hyperlink(s) to one or more journal publications or websitesthat describe the medical/biological implications, such as the presenceof a particular type of cancer, for individuals having certain DNAmethylation patterns). Thus, for example, the report can include diseaserisk or other medical/biological significance (e.g., drugresponsiveness, suggested prophylactic treatment, etc.) as well asoptionally also including the DNA methylation results, or the report canjust include DNA methylation information without including disease riskor other medical/biological significance (such that an individualviewing the report can use the DNA methylation results to determine themedical/biological significance from a source outside of the reportitself, such as from a medical practitioner, publication, website, etc.,which can optionally be linked to the report such as by a hyperlink).

A report can further be “transmitted” or “communicated” (these terms canbe used herein interchangeably), such as to the individual who wastested, a medical practitioner (e.g., a doctor, nurse, clinicallaboratory practitioner, genetic counselor, etc.), a healthcareorganization, a clinical laboratory and/or any other party or requesterintended to view or possess the report. The act of “transmitting” or“communicating” a report can be by any means known in the art, based onthe format of the report. Furthermore, “transmitting” or “communicating”a report can include delivering a report (“pushing”) and/or retrieving(“pulling”) a report. For example, reports can betransmitted/communicated by various means, including being physicallytransferred between parties (such as for reports in paper format) suchas by being physically delivered from one party to another, or by beingtransmitted electronically or in signal form (e.g., via e-mail or overthe internet, by facsimile and/or by any wired or wireless communicationmethods known in the art) such as by being retrieved from a databasestored on a computer network server, etc.

In certain embodiments, the disclosed subject matter provides computers(or other apparatus/devices such as biomedical devices or laboratoryinstrumentation) programmed to carry out the methods described herein.For example, in certain embodiments, the disclosed subject matterprovides a computer programmed to receive (i.e., as input) themethylation level at a particular genomic locus, and provide (i.e., asoutput) the risk of disease or other result (e.g., disease diagnosis orprognosis, drug responsiveness, etc.) based on methylation level atcertain genomic DNA loci. Such output (e.g., communication of diseaserisk, disease diagnosis or prognosis, drug responsiveness, etc.) can be,for example, in the form of a report on computer readable medium,printed in paper form, and/or displayed on a computer screen or otherdisplay.

Certain further embodiments of the disclosed subject matter provide asystem for determining a diagnosis or prognosis. Certain exemplarysystems include an integrated “loop” in which an individual (or theirmedical practitioner) requests a determination of such individual'scancer risk (or drug response), this determination is carried out bytesting a sample from the individual, and then the results of thisdetermination are provided back to the requester. For example, incertain systems, a sample is obtained from an individual for testing(the sample can be obtained by the individual or, for example, by amedical practitioner), the sample is submitted to a laboratory (or otherfacility) for testing (e.g., determining the methylation status level ata genomic locus), and then the results of the testing are sent to thepatient (which optionally can be done by first sending the results to anintermediary, such as a medical practitioner, who then provides orotherwise conveys the results to the individual and/or acts on theresults), thereby forming an integrated loop system for determining anindividual's cancer risk (or drug response, etc.). The portions of thesystem in which the results are transmitted (e.g., between any of atesting facility, a medical practitioner, and/or the individual) can becarried out by way of electronic or signal transmission (e.g., bycomputer such as via e-mail or the internet, by providing the results ona website or computer network server which can optionally be a securedatabase, by phone or fax, or by any other wired or wirelesstransmission methods known in the art).

In certain embodiments, the system is controlled by the individualand/or their medical practitioner in that the individual and/or theirmedical practitioner requests the test, receives the test results back,and (optionally) acts on the test results to treat the individual.

The various methods described herein, such as determining a subject'srisk of having cancer by analyzing the methylation levels of certaingenomic DNA loci, can be carried out by automated methods such as byusing a computer (or other apparatus/devices such as biomedical devices,laboratory instrumentation, or other apparatus/devices having a computerprocessor) programmed to carry out any of the methods described herein.For example, computer software (which can be interchangeably referred toherein as a computer program) can analyze the methylation levels ofcertain genomic DNA loci to determine if a patient has a particular typeof cancer, e.g., AML. Accordingly, certain embodiments of the disclosedsubject matter provide a computer (or other apparatus/device) programmedto carry out any of the methods described herein.

Kits

In certain embodiments, the presently disclosed subject matter includeskits for the practice of the methods of disclosed subject matter. Thekits can include one or more containers containing compositions topractice various methods of this subject matter. The kit can optionallyinclude a container comprising one or more of linkers and/or primers (asdescribed above), and related reagents and buffers. For example, and notby way of limitation, the primers and/or linkers can include the JHpaII24XXXX and/or the JHpaII 12XXXX primers. The kit can optionally includea container including a methylation-insensitive restriction enzyme,e.g., MspI, and/or a methylation-sensitive restriction enzyme, e.g.,HpaII. The kit can optionally include enzymes and related buffers andother reagents for performing the ligation of linkers, and/oramplification of the genomic DNA fragments, e.g., PCR (i.e., forexample, DNA polymerase, Taq polymerase, primers, linkers and/orrestriction enzymes).

In certain embodiments, the kits can also optionally include appropriatepackaging (e.g., opaque containers) or stabilizers (e.g., antioxidants)to prevent degradation of the reagents by light or other adverseconditions.

In certain embodiments, the kits can optionally include instructionalmaterials containing directions or instructions (i.e., protocols)providing for the use of the reagents in performing the disclosedmethods. While the instructional materials typically include written orprinted materials they are not limited to such. Any medium capable ofstoring such instructions and communicating them to an end user iscontemplated by this subject matter. Such media include, but are notlimited to electronic storage media (e.g., magnetic discs, tapes,cartridges, chips), optical media (e.g., CD ROM), and the like. Suchmedia can include addresses to internet sites that provide suchinstructional materials.

The following Examples are offered to more fully illustrate thedisclosure, but are not to be construed as limiting the scope thereof.

Example 1: Validation of DNA Methylation to Predict Outcome in AcuteMyeloid Leukemia Using xMELP

Introduction

A novel assay that simultaneously assesses the DNA methylation status ofeighteen prognostically important loci in patients with AML waspreviously described (15). This methodology of the novel assay was basedon the HpaII small fragment Enrichment by Ligation mediated PCR (HELP)assay and depends on molecular techniques-restriction digestion,oligonucleotide ligation and PCR—that are commonplace in a clinicalmolecular laboratory (16). Unlike HELP, which employs custom-made solidphase oligonucleotide arrays for locus identity and methylationassessment, the novel assay (termed Microsphere HELP or MELP) usesoligonucleotides-coupled fluorescent microspheres and flow cytometricanalysis for multiple loci DNA methylation assessment. Similarmicrosphere-based techniques are commonly performed for multiple-locusmutation assessment of patients with AML in clinical molecularlaboratories (17). MELP is a quantitative method for locus-specificassessment of DNA methylation and that methylation levels determined byMELP are virtually identical to those determined by HELP (15).Additionally, a MELP-based DNA methylation classifier using the sameprognostic loci previously identified with the HELP assay was able tosegregate tumors into subgroups with significantly distinct outcomes.These data suggested that MELP is a robust method of multi-locus DNAmethylation quantitation that may be useful for assessing prognosis inpatients with AML.

Novel developments have been achieved in both the assay methodology aswell as the multivariate classification algorithm used to predict AMLprognosis. Specifically, the presently disclosed optimized MELPtechnique (“xMELP”), shortens the assay time to make it more appropriatefor use in the clinical laboratory (18). Additionally, themethylation-based prognostic algorithm is now based on a random forestclassifier using a refined 17-locus panel trained from a set of 344 AMLsamples. With these new alterations, quality control standards weredefined for the assay and its performance characteristics weredescribed. Further, the xMELP assay and new classification algorithmstrongly predicted overall survival in an independent cohort of 70primary AML samples. These results indicate that xMELP is suitable forprognostic tumor evaluation in the clinical setting.

Methods and Materials

xMELP Assay

The xMELP assay was performed as follows. DNA preparation from 5 millioncells was performed with the Qiagen Puregene kit (Qiagen, Valencia,Calif.), following the manufacturer's protocol for DNA extraction frombuffy coat samples. The digest and ligation reactions are combined intoa single reaction using 500 ng of DNA along with 7.5 μl of previouslyannealed oligonucleotides (30 D/ml JHpaII 12XXXX and 60 D/μlJHpa24XXXX), 0.5 μl BSA (10 mg/ml, NEB), 0.5 μl ATP (100 mM, pH 7.0,NEB), 5 μl digestions buffer (NEB), 4 U MspI or 2 U HpaII (NEB) and 2 UT4 DNA ligase (Life Technologies). Total reaction volume is 50 μl.Reactions are carried out at 25° C. for 12 hours. Subsequent PCRamplification using JHpaII 24 XXXX primers was performed as described.For most reactions, PCR was performed in 100 mL total volume for 20cycles. For reactions in which input DNA was serially diluted, PCR with11 cycles of amplification was performed.

The primers used for xMELP were as follows (nomenclature as previouslyused with XXXX indicating xMELP primers):

(SEQ ID NO: 3) JHpaII 12XXXX: CG CCTGTTCATG (SEQ ID NO: 4)JHpaII 24XXXX: CGACGTCGACTATCCATGAACAGG

Nucleotides in bold indicate changes in the original MELP primers toprevent redigestion of ligated products. Underlined nucleotides areinvolved in annealing to genomic DNA. JHpaII24

XXXX was also used for PCR.

For dilution experiments, genomic DNA was diluted with either water tothe indicated final amounts or with genomic DNA prepared from peripheralblood of a healthy donor at indicated ratios. Ficoll preparation of bonemarrow samples was performed according to standard protocol.

Median fluorescent intensity (MFI) was measured to derive raw abundancevalues from Luminex beads as previously described (15). Log₂(HpaII/MspI)values were scaled by subtracting the mean log₂ ratio scores for threeloci (MSPI0406S00318682, MSP10406S00890278, MSPI0406S00653944) that werepreviously shown to represent an unmethylated baseline within thissample type.

Quantigene 2.0 Hybridization was performed as previously described (15).In particular, sequential hybridization reactions for complexingamplicons onto fluorescent microspheres and for branched DNA signalamplification were performed with the Quantigene 2.0 assay, followingthe manufacturer's protocol for RNA hybridization (Affymetrix).Specifically, 8 mL PCRs were incubated at room temperature using 5 mL of2.5 mol/L NaOH, 5 ml of the locus-specific probe mixture, and 5 mL oflysis mixture (the latter two products provided in the Quantigene 2.0assay) in 68 mL total volume. The reaction was neutralized by additionof 36 mL of 2 mol/L HEPES buffer. This amplicon/probe mixture was addedto a 20 mL reaction mix consisting of 0.2 mL of proteinase K, 15 mL oflysis mixture, 2 mL of blocking reagent, and 1 mL of locus specificfluorescent microspheres (all products provided in the Quantigene 2.0assay). These hybridizations were incubated with shaking at 55° C.overnight. Reactions were placed on a magnet and washed three times withwash buffer (provided in the Quantigene 2.0 assay). The reactions werethen sequentially hybridized to pre-amplifier, amplifier, andbiotinylated label-probe DNA in 100 mL of the appropriate buffer(provided in the Quantigene 2.0 assay). All hybridizations wereperformed for 1 hour at 50° C. with shaking. Each hybridization waspreceded by magnetic bead capture and three washes. Afterhybridizations, the reaction was incubated at room temperature with 4mg/mL streptavidin-phycoerythrin in the appropriate buffer (supplied bythe manufacturer). After three washes, the fluorescent microspheres wereanalyzed by flow cytometry on a FLEXMAP three-dimensional instrumentrunning xPONENT 4.0 software (Luminex Corporation, Austin, Tex.). Theentire procedure was performed separately for products derived fromMspI-digested, HpaII-digested, or mock-digested DNA. Amount of boundproduct was determined by phosphatidylethanolamine signal, whereas locusidentity was determined by fluorescence signal of each microsphere.Relative methylation was determined by the ratio ofphosphatidylethanolamine median fluorescence intensity of each locus inMspI-digested and HpaII-digested samples normalized to the same ratio ofknown hypomethylated loci.

Tumor Bank

Training data were obtained from a previously published cohort (GEOaccession GSE 18700,http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE 18700) of 344 AMLpatients collected by the Dutch-Belgian Hemato-Oncology CooperativeGroup (HOVON) for which methylation status had been measured using HELP(6). As previously described (15), a subset of these samples were rerunusing MELP and converted the HELP values to MELP-scale values usingDeming regression. This 344-sample cohort served as the training set forcreating the random forest classifier in this Example.

An independent cohort of 207 AML samples was obtained from patientsevaluated at the Perelman School of Medicine, University of Pennsylvania(Philadelphia, Pa.). All subjects provided informed consent forcollection under a protocol previously approved the University ofPennsylvania Institutional Review Board. Genomic DNA was isolated, andxMELP was performed on all 207 samples. A subset of this group (n=70)was randomly selected from the set of subjects for whom survival datawere available, and this subset was used to validate the outcomesperformance.

Locus Selection

Using the original HOVON methylation data, 18 loci have previously beenidentified as predicting outcome in AML (6), and their use with MELP hasbeen previously described (15). Additionally, the original HOVON dataset was randomly repartitioned and supervised principal components wereused as previously described to identify an additional 9 candidate loci.Methylation at these 27 loci was simultaneously assayed along with threecontrol loci in a single multiplex experiment for each AML sample (Table1 and Table 2). A subset of these loci was used in the finalclassification model (see below under Data analysis and multiplexclassification).

Data Analysis and Multiplex Classification

Data analysis was performed using the R statistical software package (Rversion 3.0.2) in conjunction with the survival (version 2.37-7),MethComp (version 1.22), and scatterplot3d (version 0.3-35) packages(19-22). In order to provide detailed documentation of the analysisprocedures, an R script capable of reproducing figures from thismanuscript has been included as supplementary data along with associateddata files. Random survival forest calculations were performed using theRandomForestSRC package (version 1.4) (23) with 1000 trees. The randomforest was trained to predict survival using the HOVON (n=344) HELP dataafter converting the HELP values to equivalent MELP scale values usingDeming regression as previously described (15).

In order to remove uninformative loci from the model, a procedurerecently described proposed by Hapfelmeier and Ulm was used (21). Thisapproach utilizes variable importance scores, which reflect thecalculated influence of each locus on overall random forest performance.Five random forests were generated using the training data set, and thedistribution of the variable importance score was tabulated for each ofthe 27 candidate loci. For each locus, a control distribution was thengenerated by randomly permuting the values of that locus across sampleswhile retaining the original values at all other (n=26) loci. Aftertraining a random forest on this permuted data set, the variableimportance for the selected locus was recorded. This procedure wasrepeated 50 times for each locus, resulting in a distribution of controlimportance scores. This distribution represents the expected importanceof each locus if it were uninformative, and the control distribution canthen be compared to the distribution of importance scores obtained whenusing the original (nonpermuted) data. To determine which locicontribute significantly to the random forest performance, a 1-tailed Ttest (actual distribution>control distribution) was used at each locus,with a locus considered “informative” if it showed aBenjamini-Hochberg-corrected P value <0.05.

17 loci passed this threshold and were utilized for the remainder of thework. After identification of the optimal subset of 17 informative loci(Table 1 and Table 2), a final random survival forest was trained. Theoutput of this random forest, which is designated as the methylationscore (M-Score), represents the estimated risk associated with a givensample. Thus, a low M-score corresponds to a long predicted survival(good prognosis), and a high M-score corresponds to a shorter predictedsurvival (poor prognosis).

Perturbation Analysis of Multiplex Classification Score

In order to determine the effect of changing the value of one or moreloci on the overall score function, Monte Carlo perturbation approachwas utilized. Briefly, random subsets of 1, 2, 3, 5, or 10 loci wereselected in order to assess the effect of different numbers of “failed”loci. Random specimens were selected from the 21 repeat samples, and foreach random subset of perturbed loci the values of each locus werereplaced with a value for the same locus drawn randomly from the fullcohort of 208 UPenn samples. In this way, a draw of random values weregenerated that were biologically plausible but incorrect. This processwas repeated 100 times for each number of perturbed loci, and summaryresults were obtained.

Results

It has been previously shown that the MELP assay can simultaneouslyassess levels of DNA methylation at multiple loci (15). Briefly, thisassay uses a methylation insensitive restriction enzyme (MspI) alongwith its methylation-sensitive isoschizomer (HpaII) to create twodifferentially digested DNA aliquots. Ligation of an oligonucleotidelinker followed by linker-specific Taq-mediated PCR generates pools ofamplicons in which relatively short, easily amplifiable productspredominate. Thus, in the HpaII digest, regions of hypomethylateddigestable DNA will be selectively amplified and will be relativelyover-represented compared to regions of hypermethylation. Amplicons arefluorescently labeled and hybridized to Luminex microspheres, which arethen subjected to flow cytometry to quantitate amplicon abundance(determined by amplicon fluorescent signal) at specific loci (determinedby Luminex microsphere fluorescence). Differential signals from the MspIand HpaII digests at each locus are used to identify the level ofmethylation in the original sample DNA. A methylation level iscalculated as log₂(HpaI/MspII), with normalization provided by theaverage methylation level of three control genomic DNA loci known to behypomethyated across virtually all AML samples. Notably, this procedureis amenable to multiplexing, and >30 loci were successfully quantitatedsimultaneously in a single Luminex experiment utilizing on two reactiontubes. Despite the utility of the MELP assay in its original form,experiments indicated that a number of improvements could improve itssuitability for implementation in the clinical laboratory.

Development of xMELP

In order to improve the turnaround time for clinical methylationassessment, the original MELP technique was simplified using a singlebase-pair substitution in the oligonucleotides used for linker ligation.In both the HELP and original MELP assays, ligating the oligonucleotidesto genomic DNA fragments recreates a HpaI/MspII site (15, 16). SinceMspI is not heat labile, the standard HELP assay requires a phenolchloroform extraction followed by EtOH precipitation after therestriction digest to prevent re-digestion of ligated products. Bychanging a single base pair in the primers used for ligation (seeMaterials and Methods), the 5′ overhang necessary for genomic DNAannealing was maintained while ensuring that the ligated products nolonger contain a HpaI/MspII restriction site. With this subtlealteration, the restriction digestion and ligation of MELP can becombined into a single step, thereby reducing the total time requiredfor performing MELP by a full day and significantly decreasing theamount of sample manipulation (FIG. 1A). It is important to note, thatthis alteration of the MELP technique (referred to now as expedited MELPor xMELP) does not alter the assessment of methylation status. As shownas in FIG. 1B, there is a nearly uniform correlation between MELP andxMELP at every locus analyzed in ten primary AML samples. Thus, xMELP isa valid surrogate of standard MELP and is the technique used in allsubsequent analyses.

Quality Control and Reproducibility for xMELP

In the context of running both MELP and xMELP, it was noted thatoccasional samples showed uniform low fluorescence signals across allloci, suggestive of either inadequate DNA quality (low MspI and HpaIIsignals) or failure of any of the subsequent reactions (low MspI orHpaII signals). To address the need for a uniform quality controlmetric, the mean individual scores (HpaII or MspI) were determinedacross all loci as a surrogate for global assay performance (FIG. 2A).The individual HpaII or MspI values obtained at the three unmethylatedcontrol loci used to normalize the results were then examined. The lowtail of the mean score distribution was associated with control locusvalues <100 (FIG. 2B, C). As a result, a quality control cutoff of 3/3control loci <100 (median intensity for Luminex analysis) wasestablished. This cutoff was used in subsequent analyses.

To assess the precision of individual locus measurements with xMELP, 21replicate frozen cellular aliquots of a diagnostic bone marrow samplewere obtained from a single AML patient. DNA was prepared from eachaliquot and was subjected the xMELP assay. Loci were selected as acombination of the original group shown to be prognostic for AML (n=18)along with an additional 9 loci that were identified as potentialadditional prognostic loci (see Materials and Methods). In order toassess both intra and inter-assay reproducibility, groups were run onthree separate days (7 independent replicates/day). Results are shown inFIG. 3. Because it is not appropriate to calculate a % CV in the case oflog₂-based scores extending below zero, the reproducibility in thecontrol samples were compared with the range of methylation scoresmeasured for these same loci in a cohort of 207 frozen AML specimenscollected at the University of Pennsylvania. As shown in FIG. 3, withthe exception of one locus (MSP10406S00697563), fluctuations in the(intra- and inter-assay) replicate samples are small (median intra-assaySD=0.29, median inter-assay SD=0.12) and are significantly less that thebiological variation seen across all samples (median range=9.4).

Expansion of Candidate Loci for AML Prognosis

Having established QC criteria and reproducibility parameters forindividual loci using xMELP, an improved classifier for AML prognosiswas next developed. Similar to previous work (15), MELP-correlated HELPvalues from the HOVON AML data set were used to train this classifier.In the current analysis, however, the entire HOVON data set was usedrather a subset for training. Further, in contrast to the previouslyreported classifiers utilizing supervised principal components analysis,a random forest classifier was utilized in order to exploit the robustproperties of ensemble machine learning methods (24). Robustclassification results using a random forest classifier were recentlyobtained to segregate myeloid neoplasms from reactive conditions (25),and a similar approach was utilized for AML survival prediction.

To assess which of the 27 potential loci should be included in the finalmodel, a recently proposed technique (26) was utilized to select aninformative subset. For each locus the distribution (n=5) of thevariable importance score in the random forest mode was compared with acontrol distribution (n=50) derived from generating random forests usingpermuted data for the given locus. Results for each of the 27 loci areshown in FIG. 4A, and 17 loci were identified that have a trueimportance significantly greater than control importance (corrected Pvalue of <0.05, FIG. 4B). The final random forest classifier wasconstructed using these 17 loci. Importantly, the locus previously shownto have poor precision characteristics (MSP10406S00697563, FIG. 3) isnot included in this model.

The predictive score generated by the random forest survival classifieris, in essence, a risk score that increases with poorer prognosis (27).To assess the precision of this aggregate prognostic indication, the 21replicate samples were utilized to generate a risk score using the 17loci in the final classifier. As shown in FIG. 5A, variation inintra-sample score is small compared to the inter-sample distributionamong the 207 UPenn AML samples, (overall precision=14.8% CV;intra-assay precision=13.6% CV; inter-assay precision=7.4% CV).Additionally, six samples were independently processed and analyzed induplicate, and methylation risk scores for all of the samples werehighly reproducible (FIG. 5B).

To further determine the effect of preanalytical sample parameters onthe methylation risk score for AML, additional characteristics of theassay that are relevant to its use as a clinical test were explored.Since the prognostic results of xMELP are obtained from mononuclear AMLblasts that have been enriched by Ficoll gradient centrifugation and arethen frozen, and since most clinical specimens are subjected to neitherFicoll enrichment nor freezing prior to processing, the effects thatthese two procedures would have on xMELP-derived methylation scores weredetermined. For this analysis, multiple fresh bone marrow samples wereobtained from five newly diagnosed AML patients. DNA from these sampleswas extracted at three points: 1) prior to any manipulation (“fresh noFicoll”), 2) after Ficoll gradient centrifugation (“fresh”), and 3)after both Ficoll gradient centrifugation and subsequent freezing instandard cryopreservation media (“frozen”). xMELP analysis was performedand the methylation risk score was obtained using the disclosed randomforest classifier. One fresh, Ficoll purified sample was eliminated fromconsideration due to QC failure. The remaining results are shown in FIG.5C and demonstrate minimal variation of methylation scores among thethree types of cellular manipulations, implying that fresh bone marrowsamples that are not Ficoll-gradient enriched for blasts are appropriatefor xMELP analysis.

These similar results were somewhat surprising, since it was consideredthat maturing granulocytes in the unmanipulated sample mightsignificantly alter the methylation score. Therefore, determination ofthe minimal blast percentage for which xMELP is valid was sought. Tothis end, varying ratios of genomic DNA from primary AML samples werecombined with DNA from normal peripheral blood and performed xMELP onthese mixtures. As shown in FIG. 5D, a 75:25 mixture of leukemic:normalDNA retains a similar MELP score to that of the leukemic sample alone,whereas a 50:50 ratio shows a substantial deviation.

Since the amount of DNA that can be obtained from a marrow or peripheralblood specimen can be highly variable, the total amount of DNA requiredfor xMELP to yield valid results were also determined. For thisanalysis, diluted genomic DNA from AML samples were serially and xMELPwas performed on the dilutions. Interestingly, all dilutions that passthe quality control criteria established for the assay show similarxMELP scores as the undiluted DNA (FIG. 7). Of note, neither samplepassed quality control standards with 2 ng of DNA, so this may be thelower limit of DNA amounts that is useful for xMELP testing.

One advantage of predictors based on multiplex measurements is that theaggregate score may be robust even if a subset of individual componentsis perturbed. To explore the characteristics of the AML classifier, theeffect of a simulated “failure” of 1, 2, 3, 5, or 10 components werecompared to the inherent score variation observed in the 21 replicates.To assess the effect of values that would be plausible but wrong, arandom replicate from the 21 available was selected, selected a randomsubset of j loci (j=1, 2, 3, 5, 10), and replaced the value at thatlocus with another value randomly chosen from the cohort of 207 U Pennsamples. This process was repeated (n=100) for each value of j, andresults were tabulated. Given the fact that the replicate sample has arisk score that is low relative to most samples in the UPenn cohort,this should provide a conservative estimate of the effects of perturbingthe assay since it is less likely that multiple perturbations will“offset” each other for the final score. As shown in FIG. 5E, the scoredistribution shifts higher toward the population mean when the number ofperturbed loci (j) is increased. However, the overall score distributionis relatively stable compared with true replicates if only a singlelocus “fails,” suggesting that the multiplex analysis provides somebuffer against changes in the methylation risk score due to analyticalproblems at a single locus.

Validation on an Independent Sample Cohort

Having demonstrated that the disclosed assay shows reproducible resultsand is robust in the presence of defined preanalytical variables, itsability to predict AML survival on a cohort of 70 subjects (subset ofthe 207 tumors) for which overall survival data was obtained wasdirectly tested. Since the classifier was trained on the HOVON data,this UPenn data set represents an independent cohort from a secondinstitution. xMELP results were used to generate methylation risk scoresusing the random forest classifier, and sorted results were divided intotertiles. Survival analysis showed a highly significant differencebetween methylation risk score-based cohorts (FIG. 6, P=0.009), thusdemonstrating the clinical validity of this assay. Note that a lowmethylation risk score correlates with a good prognosis, and a highmethylation risk score correlates with poor prognosis. Taken as a whole,these results strongly suggest that the xMELP assay can predict outcomesof patients with AML in two completely independent sets of AML samples(HOVON for training, UPenn for testing) and that MELP may have clinicalutility for prognostication of patients with AML.

Discussion

In a previous study, the MELP assay was utilized to assess DNAmethylation in select loci and showed that—at the individual locuslevel—the assay is specific for the loci of interest, linear over athree-log range of signal intensity, as quantitative as methodsinvolving real-time PCR, and capable of faithfully recapitulating levelsof DNA methylation determined by the HELP assay and MassArray EpityperAssay (15). In terms of methylation in AML, an overall methylation riskscore was demonstrated to significantly predicted outcome in a cohort ofprimary AML samples. Taken together, these results, coupled with therelatively standard techniques and instrumentation used for MELP,suggested that MELP could be a useful assay for determination ofprognostic DNA methylation patterns in AML and perhaps additionaldiseases.

The previous study has been expanded on by significantly improving thetechniques and analysis, clarifying the assay characteristics (includingprecision), establishing quality control parameters, and demonstratingthe predictive potential of xMELP in an independent set of primary AMLsamples. These results further the argument that MELP can be used formeasurement of DNA methylation in a clinical laboratory setting fordetermination of both prognosis and optimal treatment of patients withAML.

With the development of xMELP, substantial improvements to the assaytechnique, the loci used and the method of analysis were made. A singlebase-pair change in the primers used significantly reduces the amount ofhands-on work required for the assay and decreases the turnaround timeby a full day. The entire MELP assay can now be performed with aturnaround time of two days, well within the optimal temporal windowbetween AML diagnosis and initiation of chemotherapy. The MELP yieldsvirtually identical results regardless of whether leukemic blastenrichment is performed by Ficoll gradient centrifugation; thus, thesample typically received in a clinical lab-unmanipulated bone marrowaspirate is adequate for MELP analysis.

The dilution experiments with normal DNA indicate that a 25% dilutionwith normal DNA does not significantly alter methylation risk score, soa 75% blast percentage may be taken either as a cutoff for assayvalidity or for as an indication that the sample should be enriched byFicoll centrifugation. This criterion, however, may be too stringentsince the methylation patterns of non-blast cells may not be identicalto that found in normal peripheral blood cells. Similarly, the totalamount of DNA used for MELP analysis does not significantly change theMELP risk score across a range of concentrations. Thus, establishing alower limit of total DNA cannot be based on risk score reproducibility.

Decreasing the amount of source DNA, however, does increase the chancesthat the assay will fail the quality control requirements that wereestablished. Hence, performing the assay with low amounts of DNA (e.g.,<10 ng) may not be cost or time effective, but results from low amountsof DNA may yield valid results provided that they pass the establishedquality control standards. Of note, however, one sample showedsignificant deviations from standard xMELP when DNA levels were <50 ng;thus, further work may be required to fully explore the behavior ofxMELP at these boundary conditions.

The analysis of the MELP data is somewhat complex and utilizes a randomforest classifier to determine the methylation risk score. In theanalysis of the MELP data, a number of multiple-variable analyticapproaches were tried, including the SuperPC algorithm used in theoriginal HELP and MELP analysis of AML (6, 28), and found that therandom forest method yielded robust results. Further, the multiplexclassifier, described herein, has the property of retaining itspredictive value even if a single locus yields an erroneous value.

Assay precision both at the individual locus level and in terms ofoverall methylation risk is likely sufficient for clinical use. Comparedto inter-sample variation across 207 samples, the intra-samplevariability of is minimal at most loci analyzed, including all of theloci that are included in the 17-locus methylation classifier. Formaltesting of the methylation risk score variation using 21 replicates ofthe same sample shows a % CV of ˜15% and further testing showed littlevariation in six samples when tested in duplicate. Additionalindications of test reproducibility were obtained from experiments inwhich DNA from AML patients was diluted with either water or with DNAfrom normal peripheral blood. Normal blood DNA also showed a similarmethylation risk score in replicate sample measurements, againunderscoring the reproducibility of the assay. Importantly, when theUPenn AML cohort were divided into prognostic tertiles, 5/6 replicatesare found within the same tertile. Although small variations in riskscore can change tertile assignment for samples at the borders of thegroups, repeat testing of borderline specimens may ameliorate the riskof misclassification. Of course, the random forest survival analysisyields a continuous risk score, so results may be reportedquantitatively rather than categorically. Overall, these resultsindicate a high level of confidence is associated with subgroupclassification based on xMELP determined DNA methylation.

Since an AML cohort (UPenn samples) that was entirely independent of theoriginal HOVON AML cohort and was obtained collected from anotherinstitution, the full HOVON sample set was used to train a methylationbased classifier prior to testing the classifier on the UPenn data set.This scenario is distinct from previous work in which the HOVON datasetwas randomly divided into training and test subsets (6, 15). Theaddition of samples to the training set, along with the re-analysis ofHELP data for inclusion of additional informative loci, the eliminationof ultimately uninformative loci, and the use of the random forestalgorithm furthered the optimization of the xMELP risk score.Importantly, testing the xMELP AML risk score on a subset of the UPennsamples for which outcome data has been obtained clearly shows that anxMELP assay can segregate AML patients with distinct outcomes.Importantly, this validation cohort was collected at a differentinstitution and on a different continent than the training cohort. Thisanalysis is currently being expanded to include the remaining samples inthe UPenn cohort and will use the full set to conduct a multivariateanalysis to test the independent prognostic power of xMELP-derived DNAmethylation patterns.

Further studies will also attempt to develop an integrated, globalprognosis classification scheme for AML using all available factors(including DNA methylation) currently known to influence patientprognosis.

Taken together, these studies strongly suggest that xMELP, inconjunction with the analytic methods developed, is a powerful assay fordetermining outcome in patients with AML. This prognostic power, as wellas the reproducibility of xMELP, rapid turnaround time, and simplicityof the assay, demonstrates its suitability for use in clinical moleculardiagnostics laboratories as a standard test in patients with AML. Givenits general applicability, xMELP also warrants further exploration as aclinical assay (most likely utilizing other loci and bioinformaticsclassifiers) for other diseases in which DNA methylation patternsstrongly influence clinical outcomes.

TABLE 1 HELP MELP Chromo- Classi- Locus ID some Start Stop fierMSPI0406S00783415 chr17 2208021 2208391 + MSPI0406S00920592 chr2032274469 32275009 MSPI0406S00304798 chr6 3024925 3025589MSPI0406S00196536 chr3 129274773 129275235 + MSPI0406S00697563 chr14105860849 105861218 MSPI0406S00011246 chr1 11723172 11723834 +MSPI0406S00861109 chr19 1924052 1924259 + MSPI0406S00333894 chr6108615428 108615973 + MSPI0406S00754805 chr16 30538940 30539797 +MSPI0406S00613804 chr12 53661106 53661621 + MSPI0406S00176846 chr348601900 48602237 MSPI0406S00715593 chr15 65810129 65810776 +MSPI0406S00698115 chr14 106354882 106355276 + MSPI0406S00600078 chr126233715 6234255 + MSPI0406S00914183 chr20 11899205 11899843 +MSPI0406S00710190 chr15 50838542 50839225 + MSPI0406S00163833 chr38542436 8543339 + MSPI0406S00765490 chr16 68345197 68345691 +MSPI0406S00914182 chr20 11898849 11899205 + MSPI0406S00914181 chr2011898555 11898849 + MSPI0406S00997890 chrX 48795887 48797005MSPI0406S00838340 chr18 5293969 5294770 MSPI0406S00136939 chr2 158114266158115184 + MSPI0406S00669709 chr14 24867489 24867729 MSPI0406S00027418chr1 32739167 32739750 MSPI0406S00589152 chr11 118763110 118763426MSPI0406S00910305 chr20 814970 815202 MSPI0406S00708912 chr15 4500346345004002 MSPI0406S00318682 chr6 34856156 34857019 C MSPI0406S00653944chr13 53028642 53029495 C MSPI0406S00890278 chr19 37958559 37958860 CList of loci used in the xMELP assay. HELP/MELP 1 D numbers withcorresponding genomic location (hg19 assembly) are indicated. The lociused in the final classifier are shown indicated with (+). The controlloci used in the classifier are marked as “C.”

TABLE 2 Locus ID Chromosome Start Stop Locus Symbol Gene Card IDMSPI0406S00783415 chr17 2208021 2208391 SMG6 GC17M001963MSPI0406S00920592 chr20 32274459 32275009 E2F1 GC20M032263MSPI0406S00304798 chr6 3024925 3025589 — MSPI0406S00196536 chr3129274773 129275235 PLXND1 GC03M129274 MSPI0406S00697563 chr14 105860849105861218 BC127913 MSPI0406S00011246 chr1 11723172 11723834 FBXO6GC01P011724 MSPI0406S00861109 chr19 1974052 1924259 — MSPI0406S00333894chr6 108615428 108615973 LACE1 GC06P108616 MSPI0406S00754805 chr1630538940 30539797 ZNF768 GC16M030535 MSPI0406S00613804 chr12 5366110653661621 ESPL1 GC12P053662 MSPI0406S00176846 chr3 48601900 48602237 UCN2GC03M048599 MSPI0406S00715593 chr15 65810129 65810776 DPP8 GC15M065734MSPI0406S00698115 chr14 106354882 106355276 KIAA0125 GC14P106383MSPI0406S00600078 chr12 6233715 6234255 VWF GC12M006058MSPI0406S00914183 chr20 11899205 11899843 BTBD3 GC20P011866MSPI0406S00710190 chr15 50838542 50839225 USP50 GC15M050792MSPI0406S00163833 chr3 8542436 8543339 LMCD1 GC03P008518MSPI0406S00765490 chr16 68345197 68345691 SLC7A6OS/ GC16M068320/ PRMT7GC16P068344 MSPI0406S00914182 chr20 11898849 11899205 BTBD3 GC20P011866MSPI0406S00914181 chr20 11898555 11898849 BTBD3 GC20P011866MSPI0406S00997890 chrX 48795887 48797005 — MSPI0406S00838340 chr185293969 5294770 ZFP161 GC18M005289 MSPI0406S00136939 chr2 158114266158115184 GALNT5 GC02P158079 MSPI0406S00669709 chr14 24867489 24867729NYNRIN GC14P024868 MSPI0406S00027418 chr1 32739167 32739750 LCKGC01P032716 MSPI0406S00589152 chr11 118763110 118763426 CXCR5GC11P118754 MSPI0406S00910305 chr20 814970 815202 FAM110A GC20P000762MSPI0406S00708912 chr15 45003463 45004002 B2M GC15P045003MSPI0406S00318682 chr6 34856156 34857019 TAF11 GC06M035734MSPI0406S00653944 chr13 53028642 53029495 VPS36 GC13M052986MSPI0406S00890278 chr19 37958559 37958860 ZNF569 GC19M037902HELP/MELP ID numbers and their corresponding genomic location (hg19assembly), locus symbol and gene card ID.

Example 2: Optimization of the xMELP Diagnostic Assay

Introduction

The ability to predict therapeutic response is essential for improvingcare of patients with acute myeloid leukemia (AML). Establishedprognostic schemes in AML are based on 1) clinical features and 2)pre-treatment karyotype but incompletely predict outcome. Recent effortsto understand AML variability have focused on the relationship betweenepigenetic abnormalities—including changes in DNA cytosinemethylation—and AML phenotype. DNA methylation patterns differ betweenleukemic cells and normal progenitor cells, and distinct methylationsignatures have been described in AML subgroups (6, 43-44).

While the mechanism by which aberrant methylation contributes toneoplasia remains incompletely understood, epigenetic alterations showsignificant correlation with patient outcome in several hematologicmalignancies, including AML (6, 7, 10, 30-32). Despite the recognizedrelationship between DNA methylation and AML prognosis, clinicalmethylation assessment is not routine due to lack of a rapid, reliableassay and a prognostic biomarker that provides validated prognosticinformation. As described herein, a novel microsphere-based assay forsimultaneous assessment of DNA methylation status at multiple prognosticloci was developed using commonplace clinical laboratory techniques (6,15, 16). This assay—xMELP—is an adaptation of the well-established HpaIITiny Fragment Enrichment by Ligation Mediated PCR (HELP) assay. Example1 above describes the technical parameters of xMELP, includingprecision, locus specificity, analytic sensitivity and turn-around time,which are appropriate for clinical use (15, 33).

In conjunction with a 17-locus xMELP assay, a methylation risk score(M-score) was developed for AML using a random forest classificationmethod, as described in Example 1, to demonstrate the associationbetween the M-score and overall survival (OS) in a cohort of AMLpatients (33).

Methods and Materials

Study Population and Patient Samples

UPenn Cohort.

183 consecutive patients with de novo AML (34) at the University ofPennsylvania (UPenn) who consented to donation of a diagnostic sample tothe Hematologic Malignancies Tissue Bank of the University ofPennsylvania between 2001 and 2012, had adequate quality DNA foranalysis, and consented to review of their medical records. Standardmolecular (FLT3-ITD and NPM1) and cytogenetic studies were available for166 samples. Cytogenetic risk was classified according to the MedicalResearch Council (MRC) criteria (29). FLT3-ITD and NPM1 status wasassessed in a CLIA-certified lab and classified as mutant or wildtype.For 136 patients, more extensive molecular information was availablefrom targeted next-generation sequencing. Patient and diseasecharacteristics, treatment, and clinical outcomes were obtained frommedical records. Median follow-up was 68.1 months (range, 1.4 to 150.2)among 38 survivors and 10.5 months (range, 0.1 to 95.2) among those(n=128) deceased.

E1900 Cohort.

The validation cohort was comprised of 383 patients who enrolled onEastern Cooperative Oncology Group (ECOG) Trial 1900 (E1900) between2002 and 2008 who had available DNA methylation, genetic, and clinicaldata. Methylation data is publically available (Gene Expression Omnibusrepository accession number GSE24505 [http://www.ncbi.nlm.nih.gov/geo])(6). Patients with indeterminate cytogenetics were analyzed with theintermediate risk patients. Median follow-up was 83.2 months (range, 0.8to 120.4) among 108 survivors and 11.0 months (range, 0.2 to 77.5) amongthose (n=275) deceased. Institutional review board approval was obtainedfrom the University of Pennsylvania and the Eastern Cooperative OncologyGroup.

Samples, xMELP and the M-Score

DNA extraction and the xMELP assay were performed on UPenn samples asdescribed in Example 1 (See also 15, 33). The M-score of each sample wasdetermined using the random forest classification algorithm previouslytrained on an independent cohort of 344 AML samples collected by theDutch-Belgian Hemato-Oncology Cooperative Group (HOVON) (R-scripts forM-score derivation are previously described and publicly available) (asdescribed in Example 1 and reference 33). For ECOG samples (i.e., theE1900 samples), HELP-derived methylation data was transformed toMELP-associated values using previously described regressioncoefficients (15).

Treatment

UPenn Cohort.

The induction chemotherapy regimen in all cases included ananthracycline and cytarabine. Patients with residual leukemia at Nadirbone marrow assessment were frequently re-treated with ananthracycline-based regimen or high-dose cytarabine at the clinicians'discretion. The primary clinical endpoints were failure to achievecomplete remission (CR) within 90 days of induction and OS. OS was timefrom induction chemotherapy to death from any cause; for patients aliveat last follow-up OS times were censored. CR was defined as morphologicleukemic-free state on bone marrow examination after 1 or 2 cycles ofinduction chemotherapy (with assessment required to be within 90 days ofinduction chemotherapy) (34).

E1900 Cohort.

The treatment schema and endpoint definitions for the E1900 cohort havebeen previously described (35). E1900 was a randomized trial of highdose versus standard dose daunorubicin that accrued patients aged <60from 2002-2008 (NCT00049517).

Statistical Analysis

Continuous variables were summarized by median and range, andcategorical variables by count and relative frequency. Comparisons ofM-score between groups of AML patients were assessed by the parametricunpaired two-sample t-test (adjusted using Satterthwaite's method whenvariances unequal) and ANOVA test (for comparing ≧2 groups). Theassociation between M-score and blast percentage was assessed byPearson's correlation coefficient.

Univariate and multivariable logistic models were used to assess theassociation of the M-score with response to induction chemotherapy(failure to achieve CR) alone and controlling for covariates includingage, sex, white blood cell (WBC) count at diagnosis, cytogenetics andmolecular status. Survival distributions for OS were computed using theKaplan-Meier method with assessment of differences between exposuregroups computed using the log-rank test. Univariate and multivariableCox regression analyses were used to examine the association of M-scoreand OS controlling for the same covariates. Backward selection was usedin multivariable logistic and Cox models to develop the mostparsimonious model.

An optimal cut-point for the M-score was determined by identifying thecut-point that maximized the log-rank statistic between “high” and “low”M-score groups. P values were considered significant when <0.05(two-sided). Analyses were performed using Stata 12.1 (StataCorp LP).The “high” M-score was determined to be greater than 86 and the “low”M-scores were determined to be 86 or lower.

Results

M-Score is not Associated with Patient or Sample Characteristic

In total, 166 patients with de novo AML who underwent induction therapywith anthracycline and cytarabine at the University of Pennsylvania(2001-2012), had available genetic data, and donated a diagnostic sampleavailable for xMELP analysis were analyzed (Table 3). Of the 166patients, 52 (31%) were ≧60 and 35 (21%) had a WBC count at diagnosis≧100 K/uL (Table 3). The majority of patients had intermediatecytogenetic risk (13% favorable, 71% intermediate, 16% unfavorable)(Table 3). In response to 1 or 2 cycles of induction chemotherapy, 71%achieved CR and 38% were alive 2 years after starting treatment (Table4).

DNA methylation status at 17 previously identified prognostic loci wasassessed by xMELP on a diagnostic sample from each patient, and theM-score statistic was calculated using the previously trained algorithm(See Example 1 and reference 33). The mean and median M-score for theUPenn cohort was 92.3 (95% confidence interval [CI], 87.4 to 97.2) and91.4 (range, 30.8 to 197.3), respectively (FIG. 12). M-score was notsignificantly associated with patient age or gender (Table 3), specimentype (blood vs. bone marrow vs. pheresis sample, P=0.809) or blastpercentage (P=0.415).

M-Score is Significantly Associated with AML Clinical Response

In order to determine the association of M-score with AML clinicalresponse, the relationships between the M-score and both survival andability to achieve remission were examined. The distribution of M-scoreby survival status at 2 years is shown in FIG. 8. The mean M-score forsurviving patients was significantly lower (81.8) than for deceasedpatients (99.5) (81.8; 95% CI, 74.3 to 89.2 vs. 99.5; 95% CI 93.2 to105.8, P=0.0005). Similarly, the mean M-scores were 86.8 versus 105.8for those who achieved and failed to achieve CR, respectively (86.8; 95%CI, 81.3 to 92.4 vs. 105.8; 95% CI, 96.5 to 115.0, P=0.0005).Additionally, a univariate Cox survival analysis demonstrated that a10-unit increase in the M-score was associated with a 10% increase inthe hazard of death (P<0.0001, Table 5) and a 10-unit increase in the Mscore was associated with a 10% increase in the odds of failing toachieve CR (Table 6).

M-Score is Associated with OS and Failure to Achieve CR in MultivariableModels

Given the association of genetic characteristics and outcome in AML, theassociation of M-score with AML genetic characteristics were assessed(Table 3). As shown in Table 3, the M-score differed among the 3cytogenetic risk groups: the favorable cytogenetics group had a lowermean M-score than both the intermediate and unfavorable groups (P<0.0001and P=0.001, respectively) but there was no difference in mean M-scorebetween intermediate and unfavorable groups (P=1.0). Patients that had afavorable cytogenetic risk had an average M-score of 66.1; whereaspatients that had intermediate cytogenetics had a mean M-score of 95.4and those with an unfavorable cytogenetic risk had an average M-score of99.1.

The M-score was not associated with established molecular markers of AMLrisk (NPM1 and FLT3-1TD, Table 3) but was associated with mutations inDNMT3A and IDH1, two genes involved in regulation of DNA methylation.M-score was not, however, associated with mutation in other methylationregulators including IDH2, JET2, or WT1 (Table 8).

Multivariable analyses were next performed to determine if the M-scorewas independently associated with OS and CR. In multivariable Coxanalysis, higher M-score and older age were associated with increasedhazard of death, while NPM1+/FLT3-ITD-status was associated withdecreased hazard of death (Table 5). Interestingly, the reducedmultivariable model for survival included only age andNPM1+/FLT3-ITD-status in addition to M-score (Table 5). Similarly, in amultivariable logistic analysis, M-score was associated with increasedodds of failing to achieve CR. The reduced (parsimonious) multivariablemodel for failure to achieve CR included M-score, age, and cytogenetics(Table 6). The association between M-score and hazard of death and oddsof achieving CR was not significantly different between younger (<60years) and older (>60 years) patients.

Additional multivariable Cox and logistic regression analyses includingDNMT3A and IDH1 conducted on the subset of patients with extendedmolecular data (n=136) confirmed that M-score remained significantlyassociated with survival and achievement of CR (Tables 9 and 10).Notably, NPM1+/FLT3− was the only genetic marker included in bothreduced Cox models, suggesting that M-score is more strongly associatedwith AML outcome than most individual genetic markers.

Risk Classification Using the M-Score

After confirming the independent association of M-score with clinicaloutcome in AML, a risk classifier for clinical application was designed.Based on the maximization of the log-rank statistic, the optimal binaryM-score cutpoint was determined to be 86 in the UPenn cohort (FIGS. 13and 16). Using the optimal cutpoint, a binary M-score classifier wasdefined. The binary classifier identified two groups in the UPenncohort—low and high M-score groups. The Kaplan-Meier curves for the lowand high M-score groups are shown in FIG. 9 (characteristics of the 2groups are described in Table 11). A high M-score was associated with anincreased hazard of death alone (HR 2.5, P<0.0001) and after adjustmentfor all other factors (HR 1.9, P=0.003). Median survival for the low andhigh M-score groups was 26.6 and 10.6 months; 2-year OS was 56% (95% CI,43.8 to 67.3) and 24% (95% CI, 15.2 to 33.1) (Table 7). The CR rate forlow and high M-score group was 84% (95% CI, 75.2 to 92.4) and 61% (95%CI, 50.7 to 71.0; P=0.001), respectively (Table 7).

AML patients aged≦60 years with intermediate cytogenetics are inparticular need of new tools for risk stratification; therefore, thebinary M-score classifier was evaluated in this subgroup (described inTable 3). Standard prognostic factors were not different betweenpatients with low and high M-scores within this subgroup (Table 11). Theclassifier defined groups with significantly different OS (log-rankP=0.001; FIG. 10). Median survival was 36.4 versus 14.9 months in thelow and high M-score groups, respectively (Table 7). Additionally, morepatients in the low M-score group were alive at 2 years (62% vs. 30%,P=0.004) and achieved CR (91% vs. 70%, P=0.019).

Finally, to investigate whether the ability of the M-score classifier todefine groups with different OS was merely a reflection of achievementof CR, analysis was restricted to the 118 patients who had achieved CR.The M-score classifier continued to identify patients with significantlydifferent OS (log-rank P<0.00001; FIG. 14) with median survival 43.9versus 17.2 months in low and high-risk groups, respectively (Table 7).In this group, the 2-year OS was 67% in the low M-score group versus 36%in the high M-score group (P=0.001). Additionally, it was noted thatpatients with high M-score were more likely to need 2 cycles ofinduction chemotherapy than those with a low M-score to achieve CR (29%vs. 6%, P=0.001).

TABLE 3 UPenn Cohort: M-score by Patient and AML Characteristics. TotalCohort Age ≦60, Intermediate n = 166) Cytogenetics (n = 82 M-scoreM-score n % (Mean) 95% CI P n % (Mean) 95% CI P All Subjects 166 10092.3 87.4-97.2  — 82 100 94.2 87.0-101.5 — Age (years), diagnosis ≦60114 68.7 90.6 84.6-96.5  .297 — — — — — >60 52 31.3 96.2 87.4-105.0 — —— — — Sex Male 98 59.0 90.8 84.2-97.5  .476 46 56.1 92.1 81.9-102.3 .516Female 68 41.0 94.4 87.1-101.8 36 43.9 96.9 86.3-107.5 WBC (×10⁹/L),diagnosis <100 131 78.9 935 87.7-99.4  .262 59 72.0 95.4 86.3-104.6 .604≧100 35 21.1 87.8 79.3-96.2  23 28.0 91.2 79.5-102.9 Cytogenetic riskgroup* Favorable 21 12.7 66.1 57.0-75.2  .0002 — — — — — Intermediate118 71.1 95.4 89.6-101.3 — — — — — Unfavorable 27 16.3 99.1 86.9-111.3 —— — — — FLT3-ITD Mutant 56 33.7 93.4 86.5-100.2 .742 37 45.1 92.983.5-102.3 .744 Wild type 110 66.3 91.8 85.2-98.4  45 54.9 95.384.3-106.3 NPM1 Mutant 58 34.9 93.3 84.0-96.9  .549 38 46.3 89.581.1-97.9  .212 Wildtype 108 65.1 90.5 86.5-100.1 44 53.7 98.486.8-109.9 NPM1+/FLT3- ITD− Yes 25 15.1 84.3 74.9-93.7  .084 17 20.783.1 74.1-92.1  .025 No 141 84.9 93.7 88.2-99.3  65 79.3 97.1 86.4-105.9*Medical Research Council criteria (2010) AML, acute myeloid leukemia;WBC, white blood cell; FLT3-ITD, FMS-like kinase 3-internal tandemduplication; NPM1, nucleophosmin

TABLE 4 AML Treatment and Clinical Outcome for the UPenn Cohort. TotalCohort Age ≦60, Intermediate (n = 166) Cytogenetics (n = 82) n % n %First Induction Dauno 45/Ara-C 51 30.7 18 18.4 Dauno 60/Ara-C 8 4.8 00.0 Dauno 90/Ara-C 26 15.4 18 22.0 Ida 12/Ara-C 71 42.8 46 56.1 Mito10/Ara-C 9 5.4 1 2.3 Unconfirmed 2 1.2 2 1.8 Second Induction Yes 4527.1 21 25.6 Anthracymine Ara-C 26 15.7 9 11.1 MEC 11 6.6 6 7.3High-dose Ara-C 8 4.8 6 7.3 No 119 71.7 59 72.0 Unknown 2 1.2 2 1.8 CRYes 148 71.4 65 79.3 No 48 28.9 17 20.7 2-Year OS Yes 62 38.0 35 43.2 No101 52.0 46 56.8 AML, acute myeloid leukemia; Dauno 45/Ara-C, 3 oncedaily doses of daunorubicin 45 mg/m² plus 7 daily doses of cytarabine100 mg/m² by continuous infusion; Dauno 60/Ara-C, 3 once daily doses ofdaunorubicin 60 mg/m² plus 7 daily doses of cytarabine 100 mg/m² bycontinuous infusion; Dauno 90/Ara-C, 3 once daily doses of daunorubicin90 mg/m² plus 7 daily doses of cytarabine 100 mg/m² by continuousinfusion; Ida 12/Ara-C, 3 once daily doses of idarubicin 12 mg/m² plus 7daily doses of cytarabine 100 mg/m² by continuous infusion; Mito10/Ara-C; 3 once daily doses of mitoxantrone 10 mg/m² plus 7 daily dosesof cytarabine 100 mg/m² by continuous infusion; MEC, 6 once daily dosesof mitoxantrone 6 mg/m², 8 daily doses of etoposide 80 mg/m²; plus 6daily doses of cytarabine 1 g/m²; High-dose Ara-C, 1.5 or 3 gm/m² twicedaily on days 1, 3, and 5;

TABLE 5 UPenn Cohort: Cox Model for Overall Survival (n = 166, events =128). Multi- variable Reduced Univariate Adj Adj 95% Parameter HR P HR PHR CI P M-score^(#) 1.1 <.0001 1.1 .011 1.1 1.0-1.2 .002 Age^(%) 1.3<.0001 1.3 .001 1.3 1.1-1.5 <.0001 Female 1.1 .461 1.1 .579 — — — WBC,diagnosis^(#) 1.0 .856 1.0 .210 — — — Cytogenetic Risk* (referenceunfavorable) Intermediate 0.7 .085 0.7 .226 — — — Favorable 0.3 .002 0.5.067 — — — FLT3-ITD+ 1.4 .099 1.1 .733 — — — NPM1+/FLT3- 0.5 .017 0.5.031 0.5 0.3-0.8 .011 ITD− ^(#)divided by 10; ^(%)10-year increase;*Medical Research Council critera (2010) Hazard ratio, HR; CI,confidence interval; WBC, white blood cell; FLT3-ITD, FMS-like kinase3-internal tandem duplication; NPM1, nucleophosmin1

TABLE 6 UPenn Cohort: Logistic Model for Failure to Achieve CompleteRemission (n = 166, events = 48). Multi- variable Reduced Univariate AdjAdj 95% Parameter OR P OR P OR CI P M-score^(#) 1.2 .001 1.1 .034 1.21.0-1.3 .012 Age^(%) 1.5 .002 1.5 .007 1.5 1.1-2.0 .012 Female 1.2 .6421.3 .551 — — — WBC, diagnosis 1.0 .798 1.0 .329 — — — Cytogenetic Risk*(reference unfavorable Intermediate 0.4 .057 0.6 .236 0.5 0.1-1.1 .087Favorable 0.1 .008 0.1 .030 0.1 0.0-1.0 .047 FLT3-ITD+ 0.9 .666 0.5 .168— — — NPM1+/FL3-ITD− 0.4 .131 0.3 .081 — — — ^(#)divide by 10;^(%)10-year increase; *Medical Research Council criteria (2010) OR, oddsratio; CI, confidence interval; WBC, white blood cell; FLT3-ITD,FMS-like kinase 3-internal tandem duplication; NPM1, nucleophosmin1

TABLE 7 UPenn Cohort: Clinical Outcome by High versus Low M-score.Median OS CR Rate (months) 2-Year OS (%) (%) Total Cohort (n = 166) LowM-score 26.6 56% 84% High M-score 10.8 24% 61% Age ≦60 years,Intermediate Cytogenetics (n = 82) Low M-score 36.4 62% 91% High M-score14.9 30% 70% Achieved CR (n = 118) Low M-score 43.9 67% — High M-score17.2 38% — OS, overall survival; CR, complete remission

TABLE 8 Mean M-score by Mutant Status of DNA Methylation RegulatoryGenes for UPenn Cohort (n = 136). n % M-score (Mean) 95% CI P DNMT3AMutant 42 30.1 101.5 91.1-111.9 .031 Wildtype 94 69.1 66.8 81.7-94.9TET2 Mutant 19 14.0 105.3 89.9-117.0 .122 Wildtype 117 86.0 90.684.5-96.7 IDH2 Mutant 15 11.0 101.2 89.0-113.4 .275 Wildtype 121 89.091.3 85.2-97.4 IDH1 Mutant 11 8.9 118.7 85.7-141.6 .025 Wildtype 12591.9 90.5 84.9-96.1 WT1 Mutant 8 5.9 87.2 66.1-108.3 .649 Wildtype 2894.1 92.7 88.9-98.6 CI, confidence interval; DNMT3A, DNAmethyltransferase 3A; TET2, tet methylcytosine dioxygenase 2; IDH2,isocitrate dehydrogenase 2; IDH1, isocitrate dehydrogenase 2; WT1, wilmstumor1.

Validation of the M-Score Classifier in the E1900 Cohort

Validation of the M-score prognostic classifier for OS in theindependent E1900 cohort (also referred to herein as ECOG), described inTable 12, was sought. The association of M-score with characteristics ofthis cohort is described in Tables 12-14. For these patients, the meanand median M-score were similar to the UPenn cohort (98.2 (95% CI, 94.1to 102.3) and 91.8 (range, 20.0 to 204.6), respectively) (FIG. 12). Alsosimilar to the UPenn cohort, the M-score was associated with survival onmultivariable analysis (P<0.0001), while the association withachievement of CR was marginally significant (P=0.076).

The binary prognostic classifier derived in the UPenn cohortsuccessfully identified E1900 subgroups with different OS (log-rankP<0.00001, FIG. 11). The median OS in patients in the low M-score groupwas 29.5 months versus 12.6 months for those in the high M-score (FIG.11). Among patients with intermediate cytogenetics (n=249), OS was alsosignificantly different (log-rank P=0.0003) with median OS of 32.3months versus 14.1 months in the low and high M-score groups,respectively.

Since a primary objective of E1900 was to assess the impact ofdaunorubicin dose on AML outcome, the impact of treatment on patientswith low and high M-scores was assessed. High-dose daunorubicin wasfound to be beneficial for patients with high M-scores (log-rankP=0.001) but not for those with low M-scores (P=0.328; FIG. 15).

TABLE 9 UPenn Cohort: Expanded Cox Model for Overall Survival (n = 136,events = 108). Multi- variable Reduced Univariate Adj Adj 95% ParameterHR P HR P HR CI P M-score^(#) 1.1 <.0001 1.1 .002 1.1 1.1-1.2 <.0001Age^(%) 1.3 <.0001 1.3 .002 1.3 1.1-1.5 .004 Female 1.1 .503 1.0 .868 —— — WBC, 1.0 .289 1.0 .150 1.03 1.00- .035 diagnosis^(#) 1.06Cytogenetic Risk (ref unfavorable) Intermediate 0.8 .400 9.8 .414 — — —Favorable 0.4 .014 0.6 .256 — — — FLT3-ITD+ 1.8 .026 1.3 .351 — — —NPM1+/ 0.5 .033 1.3 .107 0.6 0 .048 FLT3-ITD− DNMT3A 1.3 .192 1.0 .897 —— — IDH1 1.2 .536 1.3 .464 — — — ^(#)divided by 10; ^(%)10-yearincrease; *Medical Research Council criteria (2010) HR, hazard ratio;CI, confidence interval; WBC, white blood cell; FLT3-ITD, FMS-likekinase 3-internal tandem duplication; NPM1, nucleophosmin1; DNMT3A, DNAmethyltransferase 3A; IDH1, isocitrate dehydrogenase 1

TABLE 10 UPenn Cohort: Expanded Logistic Model for Failure to AchieveComplete Remission (n = 136, events = 38). Univariate MultivariableReduced Parameter OR P Adj OR P Adj OR 95% OR P M-score^(#) 1.2 .001 1.2.036 1.2 1.1-1.4 .002 Age^(%) 1.7 .002 1.8 .002 1.7 1.2-2.5 .003 Female0.9 .886 0.7 .559 — — — WBC, diagnosis^(#) 1.0 .620 1.0 .412 — — —Cytogenetics Intermediate 0.4 .087 0.5 .180 — — — Favorable 0.1 .012 0.1.030 — — — FLT3-ITD+ 0.9 .761 0.8 .663 — — — NPM1+/FLT3- 0.4 .141 0.3.123 — — — ITD− DNMT3A 0.6 .260 0.4 .088 — — — IDH1 3.5 .051 6,4 .028 —— — ^(#)divided by 10; ^(%)10-year increase; *Medical Research Councilcriteria (2010) OR, odds ratio; CI, confidence interval; WBC, whiteblood cell; FLT3-ITD, FMS-like kinase 3-internal tandem duplication;NPM1, nucleophosmin1; DNMT3A, DNA methyltransferase 3A; IDH1, isocitratedehydrogenase 1

TABLE 11 Patient and Disease Characteristics by Optimal M-score for theUPenn Cohort. Age ≦60 years Intermediate Total Cohort Cytogenetic RiskAchieved CR Low High Low High Low High M-score M-score M-score M-scoreM-score M-score (n =74) (n = 92) (n = 35) (n = 47) (n = 62) (n = 56) % %P % % P % % P Age ≦60 years 77.0 62.0 .037 — — — 82.3 66.1 .044 >60years 23.0 38.0 — — — 17.7 33.9 Sex Male 62.2 56.5 .463 60.0 53.2 .53962.9 57.1 .523 Female 37.8 43.5 40.0 46.8 37.1 42.9 WBC (×10⁹/L),diagnosis <100 79.7 78.3 .818 74.3 70.2 .685 82.3 75.0 .335 ≧100 20.321.7 25.7 29.8 17.7 25.0 Cytogenetic risk group* Favorable 25.7 2.2<.0001 — — — 29.0 3.6 .001 Intermediate 60.8 79.3 — — — 59.7 83.9Unfavorable 13.5 18.5 — — — 11.3 12.5 FLT3-ITD Mutant 25.7 40.2 .04940.0 48.9 .421 72.6 57.1 .079 Wildtype 74.3 59.8 60.0 51.1 27.4 42.9NPM1 Mutant 32.4 37.0 .543 54.3 40.4 .213 61.3 55.4 .514 Wildtype 67.763.0 45.7 59.6 38.7 44.6 NPM1+, FLT3-ITD− Yes 17.6 13.0 .418 28.6 14.9.131 79.0 85.7 .353 No 82.4 87.0 71.4 85.1 21.0 14.3 *Medical ResearchCouncil criteria (2010) AML, acute myeloid leukemia; WBC, white bloodcell; FLT3-ITD, FMS-like kinase 3-internal tandem duplication; NPM1,nucleophosmin1

TABLE 12 E1900 Cohort: M-score by Patient, Disease, and SampleCharacteristics (n = 383). M-score n % (Mean) 95% CI P All Subjects 383— 98.2 94.1-102.3 — Sex Male 201 52.5 96.3 90.8-101.8 .347 Female 18247.5 100.2 94.0-106.5 WBC (× 10⁹/L), diagnosis  <100 360 94.0 97.293.0-101.5 .06 ≧100 23 6.0 113.6 95.9-131.4 Cytogenetic risk group*Favorable 66 17.2 65.1 59.3-70.9  <.0001 Intermediate 254 66.3 103.498.3-108.4 Unfavorable 63 16.5 112.0 102.1-122.0  FLT3-ITD Mutant 11329.5 111.9 87.8-97.2  <.0001 Wildtype 270 70.5 92.5 103.9-119.8  NPM1Mutant 115 30.0 107.2 99.7-114.8 .005 Wildtype 268 70.0 94.3 89.4-99.2 NPM1+/FLT3-ITD- Yes 66 17.2 98.0 88.8-107.2 .964 No 317 82.8 98.293.6-102.9 Induction Treatment Standard Dose 188 49.1 98.0 92.5-104.2.946 High Dose 195 50.9 98.4 92.2-103.9 *Slovak et al. (2000) CI,confidence interval; AML, acute myeloid leukemia; WBC, white blood cell;FLT3-ITD, FMS-like kinase 3-internal tandem duplication; NPM1,nucleophosmin1

Discussion

Multivariable models incorporating standard AML prognosticcharacteristics have only a modest ability to predict clinical outcome,and the addition of molecular markers adds little to these models(40-42). To improve AML prognostication, a clinically applicable assayfor multi-locus methylation assessment (xMELP) along with acorresponding statistic (M-score) was developed (15, 33). As shown inthis Example, the M-score was associated with CR and OS in bothunivariate and multivariable models in the UPenn cohort. Without beinglimited to a particular theory, in this cohort, a reduced model for OSis based solely on the M-score, patient age, and NPM1+/FLT3-ITD-status;other information, including cytogenetics and presence of a FL T3-ITDmutation, provided little additional prognostic value. The associationbetween M-score and recurrent intragenic mutations were also explored inthe subgroup of UPenn patients with available extended mutation profilesobtained by next-generation sequencing. Mean M-score was higher inDNMT3A mutant patients and in the small group of IDH1 mutant patients,but multivariable analyses including DNMT3A and IDH1 confirmed theindependent association of M-score and clinical outcomes.

Determining the mutational profile in AML will remain important toclinical care, particularly in settings where mutations are able topredict response to targeted agents; however, clinical use of the xMELPassay and associated M-score may decrease the need for comprehensivegenetic testing for risk stratification at diagnosis (36-39). Thedisclosed reduced multivariable models indicate that the M-score has astronger association with clinical outcome than many establishedprognostic factors, including cytogenetics and FLT3-ITD status, as wellas other genetic lesions now commonly assessed by next-generationsequencing analysis.

Cox regression analyses showed strong association between the M-scoreand clinical outcomes; however, it is difficult to apply continuousmeasures of association in clinical practice. The binary M-scoreclassifier, which was validated in multiple clinically importantsubgroups and an independent cohort, clearly enhances the usefulness ofthe M-score for practicing clinicians. Additionally, the differentresponses to daunorubicin seen in M-score defined groups suggests thatM-score may correlate with chemoresistance and identify patients thatcould benefit from high-dose chemotherapy.

It is important to recognize that the loci contributing to the M-scoredo not account for all sites subject to aberrant methylation in AML.These specific loci in combination represent a marker of prognosisrather than a description of abnormal methylation or an explanatorymodel of AML biology. The prognostic value of the M-score for patientswith AML arising in the setting of prior chemotherapy or myelodysplasia,or those treated with non-intensive regimens including hypomethylatingagents, are areas of further research. Additionally, no informationregarding the association between M-score and other prognostic markers,including minimal residual disease status was obtained (39).

In summary, the M-score provides valuable information in the clinicalsetting regarding the likelihood of long-term survival after AMLinduction. Those patients predicted to have poor outcomes based onM-score may be better served with more intensive post-remissiontreatment or enrollment on a clinical trial.

TABLE 13 E1900 Cohort: Cox Model for Overall Survival (n = 383, events =275) Univariate Multivariable Reduced Parameter HR P Adj HR P Adj HR 95%CI P M-score^(#) 1.1 <.0001 1.1 <.0001 1.1 1.05-1.1  <.0001 Age^(%) 1.2.004 1.1 .015 1.1 1.03-1.3  .010 Female 0.9 .443 0.9 .287 — — — WBC,diagnosis^(#) 1.1 <.0001 1.05 .003 1.05 1.02-1.03 .001 Cytogenetic Risk(reference unfavorable) Intermediate 0.4 <.0001 0.5 <.0001 0.5 0.4-0.7<.0001 Favorable 0.3 <.0001 0.4 <.0001 0.4 0.2-0.6 <.0001 FLT3-ITD+ 1.6<.0001 1.1 .383 — — — NPM1+/FLT3-ITD− 0.5 <.0001 0.5 <.0001 0.4 0.3-0.6<.0001 ^(#)divided by 10; ^(%)10-year increase; *Slovak et al. (2000)HR, hazard ratio; CI, confidence interval; WBC, white blood cell;FLT3-ITD, FMS-like kinase 3-internal tandem duplication; NPM1,nucleophosmin1

TABLE 14 E1900 Cohort: Logistic Model for Failure to Achieve CompleteRemission (n = 383, events = 140) Multi variable Reduced Univariate AdjAdj 95% Parameter OR P OR P OR CI P M-score^(#) 1.1 .001 1.05 .076 — — —Age^(%) 1.1 .127 1.1 .369 — — — Female 0.8 .241 0.8 .251 — — — WBC, 1.1.045 1.1 .066 — — — Diagnosis* Cytogenetic Risk (ref unfavorable)Intermediate 0.6 .054 0.8 .508 0.7 0.4-1.3 .32 Favorable 0.2 <.0001 0.3.006 0.2 0.1-0.5 <.0001 FLT3-ITD+ 1.6 .044 0.9 .674 — — — NPM1+/ 0.3.001 0.3 .001 0.3 0.1-0.6 <.0001 FLT3-ITD− ^(#)divided by 10,^(%)10-year increase, *Slovak et al. (200) OR, odds ratio; CI,confidence interval; WBC, white blood cell; FLT3-ITD, FMS-like kinase3-internal tandem duplication; NPM1, nucleophosmin1; WT, wildtype; ref,reference

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Various publications, patents and patent applications are cited herein,the contents of which are hereby incorporated by reference in theirentireties.

What is claimed is:
 1. A method for determining the methylation statusof one or more genomic DNA loci: (a) obtaining a biological sample froma subject; (b) isolating genomic DNA from one or more cells of thebiological sample; (c) digesting a first sample of the genomic DNA witha methylation-insensitive restriction enzyme to form a first sample ofgenomic DNA fragments; (d) amplifying the first sample of genomic DNAfragments to generate a first sample of amplified DNA fragments; (e)hybridizing a genomic DNA locus-specific probe to the amplified DNAfragments of the first sample; and (f) quantifying the amplified DNAfragments of the first sample hybridized to the locus-specific probe. 2.The method of claim 1 further comprising digesting a second sample ofthe genomic DNA with a methylation-sensitive restriction enzyme to forma second sample of genomic DNA fragments; amplifying the second sampleof genomic DNA fragments to generate a second sample of amplified DNAfragments; hybridizing a genomic DNA locus-specific probe to theamplified DNA fragments of the second sample; and quantifying theamplified DNA fragments of the second sample hybridized to thelocus-specific probe.
 3. The method of claim 3 further comprisingcomparing the amount of amplified DNA fragments in the second samplehybridized to the locus-specific probe to the amount of amplified DNAfragments in the first sample hybridized to the locus-specific probe todetermine the methylation status of the genomic DNA locus thatcorresponds to the locus-specific probe.
 4. The method of claim 1, 2 or3, wherein the quantifying of amplified DNA fragments is performed bycomplexing the genomic DNA locus-specific probe and amplified DNAfragments to microspheres followed by flow cytometry.
 5. The method ofclaim 1, wherein the methylation-insensitive restriction enzyme is MspI.6. The method of claim 2, wherein the methylation-sensitive restrictionenzyme is HpaII.
 7. A diagnostic method comprising: (a) obtaining abiological sample from the subject; (b) determining the methylationstatus of one or more genomic DNA loci in one or more cells of thebiological sample; and (c) diagnosing the disease and/or disorder in thesubject, wherein the methylation status of the one or more genomic DNAloci indicates the presence of the disease and/or disorder in thesubject.
 8. A method for diagnosing acute myeloid leukemia (AML) in asubject comprising: (a) obtaining a biological sample from the subject;(b) determining the methylation status of one or more genomic DNA lociin one or more cells of the biological sample; and (c) diagnosing AML inthe subject, wherein the methylation status of the one or more genomicDNA loci indicates the presence of AML in the subject.
 9. A method fordetermining the prognosis of a subject that has a disease and/ordisorder comprising: (a) obtaining a biological sample from the subject;(b) determining the methylation status of one or more target genomic DNAloci in one or more cells of the biological sample; and (c) providing adisease and/or disorder prognosis based on the methylation status of theone or more genomic DNA loci in the subject.
 10. A method fordetermining the prognosis of a subject that has acute myeloid leukemia(AML) comprising: (a) obtaining a biological sample from the subject;(b) determining the methylation status of one or more target genomic DNAloci in one or more cells of the biological sample; and (c) providing anAML prognosis based on the methylation status of the one or more genomicDNA loci in the subject.
 11. The method of any of the claims 1-10,wherein the one or more genomic loci are selected from the groupconsisting of Chr 17: 2208021 to 2208391 (MSP10406S00783415); Chr 20:32274469 to 32275009 (MSPI0406S00920592); Chr 6: 3024925 to 3025589(MSP10406S00304798); Chr 3: 129274773 to 129275235 (MSPI0406S00196536);Chr 14: 105860849 to 105861218 (MSPI0406S00697563); Chr 1: 11723172 to11723834 (MSPI0406S00011246); Chr 19: 1924052 to 1924259(MSPI0406S00861109); Chr 6: 108615428 to 108615973 (MSP10406S00333894);Chr 16: 30538940 to 30539797 (MSPI0406S00754805); Chr 12: 53661106 to53661621 (MSP10406S00613804); Chr 3: 48601900 to 48602237(MSPI0406S00176846); Chr 15: 65810129 to 65810776 (MSPI0406S00715593);Chr 14: 106354882 to 106355276 (MSPI0406S00698115); Chr 12: 6233715 to6234255 (MSP10406S00600078); Chr 20: 11899205 to 11899843(MSP10406S00914183); Chr 15: 50838542 to 50839225 (MSPI0406S00710190);Chr 3: 8542436 to 8543339 (MSP10406S00163833); Chr 16: 68345197 to68345691 (MSPI0406S00765490); Chr 20: 11898849 to 11899205(MSP10406S00914182); Chr 20: 11898555 to 11898849 (MSP10406S00914181);Chr X: 48795887 to 48797005 (MSPI0406S00997890); Chr 18: 5293969 to5294770 (MSPI0406S00838340); Chr 2: 158114266 to 158115184(MSPI0406S00136939); Chr 14: 24867489 to 24867729 (MSP10406S00669709);Chr 1: 32739167 to 32739750 (MSP10406S00027418); Chr 11: 118763110 to118763426 (MSP10406S00589152); Chr 20: 814970 to 815202(MSPI0406S00910305); Chr 15: 45003463 to 45004002 (MSPI0406S00708912);Chr 6: 34856156 to 34857019 (MSPI0406S00318682); Chr 13: 53028642 to53029495 (MSPI0406S00653944); Chr 19: 37958559 to 37958860(MSPI0406S00890278); or combinations thereof.
 12. The method of any ofthe claims 1-10, wherein determining the methylation status is performedby the method of claim 1, 2, 3, 4, 5 or
 6. 13. A method for determiningthe methylation status of one or more genomic DNA loci: (a) obtaining abiological sample from a subject; (b) isolating genomic DNA from one ormore cells of the biological sample; (c) digesting a first sample of thegenomic DNA with a methylation-insensitive restriction enzyme to form afirst sample of genomic DNA fragments; (d) ligating a linker to thegenomic DNA fragments of the first sample; (e) amplifying the firstsample of genomic DNA fragments to generate a first sample of amplifiedDNA fragments; (f) hybridizing a genomic DNA locus-specific probe to theamplified DNA fragments of the first sample; and (g) quantifying theamplified DNA fragments of the first sample hybridized to thelocus-specific probe.
 14. The method of claim 13, wherein the digestionof the first sample of genomic DNA and the ligation of the linkers tothe genomic DNA fragments of the first sample are performed in a singlereaction.
 15. The method of claim 13, wherein the linker comprises anucleic acid sequence of SEQ ID NO: 3 hybridized to a nucleic acidsequence of SEQ ID NO:
 4. 16. The method of claim 13, further comprisingthe hybridizing the genomic DNA locus-specific probe to a microsphereprior to quantifying the amplified DNA fragments.